Biomarker identification

ABSTRACT

Disclosed are method and apparatus for identifying biomarkers and in particular for identifying biomarkers for use in making clinical assessments, such as early diagnostic, diagnostic, disease stage, disease severity, disease subtype, response to therapy or prognostic assessments. In one particular example, the techniques are applied to allow assessments of patients suffering from, suspected of suffering from, or with clinical signs of SIRS (Systemic Inflammatory Response Syndrome) being either infection-negative SIRS or infection-positive SIRS.

RELATED APPLICATION

This application claims priority to Australian Provisional ApplicationNo. 2013902243 entitled “Biomarker Identification”, filed on 20 Jun.2013, the subject matter of which is hereby incorporated herein byreference in its entirety.

FIELD OF THE INVENTION

The present invention relates to a method and apparatus for identifyingbiomarkers and in particular for identifying biomarkers for use inmaking clinical assessments, such as early diagnostic, diagnostic,disease stage, disease severity, disease subtype, response to therapy orprognostic assessments. In one particular example, the techniques areapplied to allow assessments of patients suffering from, suspected ofsuffering from, or with clinical signs of SIRS (Systemic InflammatoryResponse Syndrome) being either infection-negative SIRS (inSIRS) orinfection-positive SIRS (ipSIRS).

DESCRIPTION OF THE PRIOR ART

The reference in this specification to any prior publication (orinformation derived from it), or to any matter which is known, is not,and should not be taken as an acknowledgement or admission or any formof suggestion that the prior publication (or information derived fromit) or known matter forms part of the common general knowledge in thefield of endeavour to which this specification relates.

The analysis of gene expression products for diagnostic purposes isknown. Such analysis requires identification of one or more genes thatcan be used to generate a signature for use in distinguishing betweendifferent conditions. However, such identification can require theanalysis of many gene expression products, which can be mathematicallycomplex, computationally expensive and hence difficult. Much of thebiomarker discovery process is devoted to identifying a subset of thedata that may have relevant import, from which a signature is derivedusing a combination of these values to produce a model for diagnostic orprognostic use.

WO2004044236 describes a method of determining the status of a subject.In particular, this is achieved by obtaining subject data includingrespective values for each of a number of parameters, the parametervalues being indicative of the current biological status of the subject.The subject data are compared to predetermined data that includes valuesfor at least some of the parameters and an indication of the condition.The status of the subject, and in particular, the presence and/orabsence of the one or more conditions, can then be determined inaccordance with the results of the comparison.

SUMMARY OF THE INVENTION

In one aspect, the present invention provides apparatus for identifyingbiomarkers, the apparatus including an electronic processing devicethat:

-   -   uses reference data from a plurality of individuals to define a        number of groups of individuals, the reference data including        measurements of the activity of a plurality of reference        biomarkers;    -   uses a plurality of analysis techniques to identify a number of        potential biomarkers from the plurality of reference biomarkers        that are potentially useful for distinguishing the groups of        individuals, allowing the potential biomarkers to be used in        generating signatures for use in clinical assessments.

Suitably, the electronic processing device, for each analysis technique:

-   -   using the analysis technique, identifies a number of reference        biomarkers that best distinguish the groups of individuals;    -   determines if the predictive performance of the identified        reference biomarkers exceeds a predetermined threshold; and,    -   in response to a successful determination, determines the        identified reference biomarkers to be potential biomarkers.

In some embodiments, the number of reference biomarkers is at least oneof:

-   -   less than 10;    -   more than 1;    -   between 2 and 8; and,    -   5.

In some embodiments, the predetermined threshold is at least one of:

-   -   at least 90%;    -   at least 85%; and,    -   at least 80%.

Suitably, the electronic processing device:

-   -   adds potential biomarkers to a potential biomarker collection;        and,    -   removes the potential biomarkers from a reference biomarker        collection.

Suitably, for each of a plurality of analysis techniques the electronicprocessing device repeatedly identifies reference biomarkers aspotential biomarkers until the predictive performance of the identifiedreference biomarkers falls below the predetermined threshold.

The electronic processing device may iteratively identify potentialbiomarkers.

In some embodiments, the electronic processing device uses a number ofiterations including at least one of:

-   -   at least 100;    -   at least 500;    -   at least 1000;    -   at least 2000; and,    -   at least 5000.

The electronic processing device may repeatedly determine potentialbiomarkers until a predetermined number of potential biomarkers areidentified.

Suitably, the predetermined number of potential biomarkers includes atleast one of:

-   -   at least 100;    -   less than 500;    -   about 200.

In some embodiments, the analysis techniques include at least one of:

-   -   regression techniques;    -   correlation analysis; and,    -   a combination of regression and correlation techniques.

Suitably, the analysis techniques include:

-   -   sparse PLS;    -   random forest; and,    -   support vector machines.

In some embodiments, the electronic processing device:

-   -   removes a validation subgroup from the reference data prior to        determining the potential biomarkers;    -   determines the potential biomarkers using the reference data        without the validation subgroup; and,    -   uses the validation subgroup to validate at least one of:        -   the potential biomarkers; and,        -   signatures including a number of the potential biomarkers.

In some embodiments, the processing system determines the number ofgroups by classifying the individuals using at least one of:

-   -   an indication of a presence, absence, degree, or stage, or        progression of a condition;    -   phenotypic traits associated with the individuals;    -   genetic information associated with the individuals;    -   biomarkers associated with the individuals.

Suitably, the processing system determines groups at least in part usinginput commands from a user.

The reference data may include time series data indicative of theprogression of a condition.

In some embodiments, the time series data is indicative of whether acondition that is at least one of:

-   -   improving;    -   worsening; and,    -   static.

The reference data may include for each of the individuals an indicationof at least one of:

an activity of each of the reference biomarkers;

-   -   a degree of a condition;    -   a stage of a condition;    -   a presence of a condition;    -   an absence of a condition;    -   an indication of a condition progression;    -   phenotypic information;    -   genetic information; and,    -   a SOFA score.

In some embodiments, the electronic processing device identifies anumber of potential biomarkers for use as signature biomarkers, thesignature biomarkers being used in generating the signatures.

Suitably, the electronic processing device:

-   -   determines a clinical assessment; and,    -   identifies the signature biomarkers for the clinical assessment.

Suitably, the electronic processing device:

-   -   determines second groups of individuals relevant to the clinical        assessment;    -   using a second analysis technique, identifies a number of the        potential biomarkers that best distinguish the second groups of        individuals;    -   determines if the predictive performance of the identified        potential biomarkers exceeds a predetermined threshold; and,    -   in response to a successful determination, determines the        identified potential biomarkers to be signature biomarkers.

In some embodiments, the electronic processing device, in response to anunsuccessful determination:

-   -   modifies parameters of the second analysis technique; and,    -   uses the second analysis technique to identify alternative        potential biomarkers.

In some embodiments, the electronic processing device:

-   -   determines if the identified potential biomarkers are to be        excluded; and,    -   in response to a successful determination:    -   removes the potential biomarkers from a potential biomarker        database;

and,

-   -   uses the second analysis technique to identify alternative        potential biomarkers for use as signature biomarkers.

Suitably, the second analysis technique includes at least one of:

-   -   ordinal regression and,    -   support vector machines.

In some embodiments, the signatures are indicative of:

-   -   activities of each of a number of signature biomarkers; and,    -   at least one of:    -   a SOFA score; and,    -   a presence, absence, degree, or stage, or progression of a        condition.

The signatures may be indicative of a presence, absence, degree, orstage or progression of at least one of:

-   -   infection-negative SIRS; and,    -   infection-positive SIRS.

In some embodiments, activities of at least some of the potentialbiomarkers are indicative of at least one of:

-   -   a presence, absence, degree, or stage, or progression of SIRS;    -   a healthy diagnosis;    -   a presence, absence, degree, or stage, or progression of        infection positive SIRS; and,    -   a presence, absence, degree, or stage, or progression of        infection negative SIRS.

Suitably, an activity of biomarkers are indicative of a level orabundance of a molecule selected from one or more of:

-   -   A nucleic acid molecule;    -   A proteinaceous molecule;    -   An amino acid    -   A carbohydrate;    -   A lipid;    -   A steroid;    -   An inorganic molecule;    -   An ion;    -   A drug;    -   A chemical;    -   A metabolite;    -   A toxin;    -   A nutrient;    -   A gas;    -   A cell;    -   A pathogenic organism; and,    -   A non pathogenic organism.

In another aspect, the present invention provides a method fordetermining the likelihood of the presence or absence of a conditionselected from a healthy condition (e.g., a normal condition or one inwhich inSIRS and ipSIRS are absent), SIRS generally (i.e., notdistinguishing between inSIRS or ipSIRS), inSIRS or ipSIRS, or to assessthe likelihood of the presence, absence or risk of development of astage of ipSIRS (e.g., a stage of ipSIRS with a particular severity),the method comprising: (1) correlating a reference Inflammatory ResponseSyndrome (IRS) biomarker profile with the presence or absence of acondition selected from a healthy condition, SIRS, inSIRS, ipSIRS, or aparticular stage of ipSIRS, wherein the reference IRS biomarker profileevaluates at least one IRS biomarker; (2) obtaining an IRS biomarkerprofile of a sample from a subject, wherein the sample IRS biomarkerprofile evaluates for an individual IRS biomarker in the reference IRSbiomarker profile a corresponding IRS biomarker; and (3) determining alikelihood of the subject having or not having the condition based onthe sample IRS biomarker profile and the reference IRS biomarkerprofile, wherein an individual IRS biomarker is an expression product ofan IRS biomarker gene selected from the group consisting of: TLR5;CD177; VNN1; UBE2J1; IMP3; RNASE2//LOC643332; CLEC4D; C3AR1; GPR56;ARG1; FCGR1A//FCGR1B//FCGR1C; C11orf82; FAR2; GNLY; GALNT3; OMG;SLC37A3; BMX//HNRPDL; STOM; TDRD9; KREMEN1; FAIM3; CLEC4E; IL18R1;ACER3; ERLIN1; TGFBR1; FKBP5//LOC285847; GPR84; C7orf53; PLB1; DSE;PTGDR; CAMK4; DNAJC13; TNFAIP6;FOXD4L3//FOXD4L6//FOXD4//FOXD4L1//FOXD4L2//FOXD4L4//FOXD4L5;MMP9//LOC100128028; GSR; KLRF1; SH2D1B; ANKRD34B; SGMS2; B3GNT5//MCF2L2;GK3P//GK; PFKFB2; PICALM; METTL7B; HIST1H4C; C9orf72; HIST1H3I; SLC15A2;TLR10; ADM; CD274; CRIP1; LRRN3; HLA-DPB1; VAMP2; SMPDL3A; IFI16; JKAMP;MRPL41; SLC1A3; OLFM4; CASS4; TCN1; WSB2; CLU; ODZ1; KPNA5; PLACE; CD63;HPSE; C1orf161; DDAH2; KLRK1//KLRC4; ATP13A3; ITK; PMAIP1; LOC284757;GOT2; PDGFC; B3GAT3; HIST1H4E; HPGD; FGFBP2; LRRC70//IPO11;TMEM144//LOC285505; CDS2; BPI; ECHDC3; CCR3; HSPC159; OLAH;PPP2R5A//SNORA16B; TMTC1; EAF2//HCG11//LOC647979; RCBTB2//LOC100131993;SEC24A//SAR1B; SH3PXD2B; HMGB2; KLRD1; CHI3L1; FRMD3; SLC39A9; GIMAP7;ANAPC11; EXOSC4; gene for IL-1beta-regulated neutrophil survival proteinas set forth in GenBank Accession No. AF234262; INSIG1; FOLR3//FOLR2;RUNX2; PRR13//PCBP2; HIST1H4L; LGALS1; CCR1; TPST1; HLA-DRA; CD163;FFAR2; PHOSPHO1; PPIF; MTHFS; DNAJC9//FAM149B1//RPL26; LCN2; EIF2AK2;LGALS2; SIAE; AP3B2; ABCA13; gene for transcript set forth in GenBankAccession No. AK098012; EFCAB2; HIST1H2AA; HINT1; HIST1H3J; CDA; SAP30;AGTRAP; SUCNR1; MTRR; PLA2G7; AIG1; PCOLCE2; GAB2; HS2ST1//UBA2;HIST1H3A; C22orf37; HLA-DPA1; VOPP1//LOC100128019; SLC39A8; MKI67;SLC11A1; AREG; ABCA1; DAAM2//LOC100131657; LTF; TREML1; GSTO1; PTGER2;CEACAM8; CLEC4A; PMS2CL//PMS2; RETN; PDE3B; SULF2; NEK6//LOC100129034;CENPK; TRAF3; GPR65; IRF4; MACF1; AMFR; RPL17//SNORD58B; IRS2; JUP;CD24; GALNT2; HSP90AB1//HSP90AB3P//HSP90AB2P; GLT25D1; OR9A2; HDHD1A;ACTA2; ACPL2; LRRFIP1; KCNMA1; OCR1; ITGA4//CERKL;EIF1AX//SCARNA9L//EIF1AP1; SFRS9; DPH3; ERGIC1; CD300A; NF-E4; MINPP1;TRIM21; ZNF28; NPCDR1; gene for protein FL321394 as set forth in GenBankAccession No. BC013935; gene for transcript set forth in GenBankAccession No. AK000992; ICAM1; TAF13; P4HA1//RPL17; C15orf54; KLHL5;HAL; DLEU2//DLEU2L; ANKRD28; LY6G5B//CSNK2B;KIAA1257//ACAD9//LOC100132731; MGST3; KIAA0746; HSPB1//HSPBL2; CCR4;TYMS; RRP12//LOC644215; CCDC125; HIST1H2BM; PDK4; ABCG1; IL1B; THBS1;ITGA2B; LHFP; LAIR1//LAIR2; HIST1H3B; ZRANB1; TIMM10; FSD1L//GARNL1;HIST1H2AJ//HIST1H2AI; PTGS1; gene for transcript set forth in GenBankAccession No. BC008667; UBE2F//C20orf194//SCLY; HIST1H3C; FAM118A;CCRL2; E2F6; MPZL3; SRXN1; CD151; HIST1H3H; FSD1L; RFESD//SPATA9; TPX2;S100B; ZNF587//ZNF417; PYHIN1; KIAA1324; CEACAM6//CEACAM5; APOLD1;FABP2; KDM6B//TMEM88;IGK@//IGKC//IGKV1-5//IGKV3D-11//IGKV3-20//IGKV3D-15//LOC440871//LOC652493//LOC100291464//LOC652694//IGKV3-15//LOC650405//LOC100291682;MYL9; HIST1H2BJ; TAAR1; CLC; CYP4F3//CYP4F2; CEP97; SON; IRF1; SYNE2;MME; LASS4; DEFA4//DEFA8P; C7orf58; DYNLL1; gene for transcript setforth in GenBank Accession No. AY461701; MPO; CPM; TSHZ2; PLIN2;FAM118B; B4GALT3; RASA4HRASA4PHRASA4B//POLR2J4//LOC100132214;CTSL1//CTSLL3; NP; ATF7; SPARC; PLB1; C4orf3; POLE2; TNFRSF17; FBXL13;PLEKHA3; TMEM62//SPCS2//LOC653566; RBP7; PLEKHF2; RGS2;ATP6V0D1//LOC100132855; RPIA; CAMK1D; IL1RL1; CMTM5; AIF1; CFD; MPZL2;LOC100128751; IGJ; CDC26; PPP1R2//PPP1R2P3; IL5RA; ARL17P1//ARL17;ATP5L//ATP5L2; TAS2R31; HIST2H2BF//HIST2H3D; CALM2//C2orf61; SPATA6;IGLV6-57; C1orf128; KRTAP15-1; IF144;IGL@//IGLV1-44//LOC96610//IGLV2-23//IGLC1//IGLV2-18//IGLV5-45//IGLV3-25//IGLV3-12//IGLV1-36//IGLV3-27//IGLV7-46//IGLV4-3//IGLV3-16//IGLV3-19//IGLV7-43//IGLV3-22//IGLV5-37//IGLV10-54//IGLV8-61//LOC651536;gene for transcript set forth in GenBank Accession No. BCO34024; SDHC;NFXL1; GLDC; DCTN5; and KIAA0101//CSNK1G1

In some embodiments, the method determines the likelihood that SIRS or ahealthy condition is present or absent in the subject, and wherein themethod comprises: 1) providing a correlation of a reference IRSbiomarker profile with the presence or absence of SIRS or the healthycondition, wherein the reference biomarker profile evaluates at leastone IRS biomarker selected from CD177, CLEC4D, BMX, VNN1, GPR84, ARG1,IL18R1, ERLIN1, IMP3, TLR5, UBE2J1, GPR56, FCGR1A, SLC1A3, SLC37A3,FAIM3, C3AR1, RNASE2, TNFAIP6, GNLY, OMG, FAR2, OLAH, CAMK4, METTL7B,B3GNT5, CLEC4E, MMP9, KREMEN1, GALNT3, PTGDR, TDRD9, GK3P, FKBP5, STOM,SMPDL3A, PFKFB2, ANKRD34B, SGMS2, DNAJC13, LRRN3, SH2D1B, C1orf161,HIST1H4C, IF116, ACER3, PLB1, C9orf72, HMGB2, KLRK1, C7orf53, GOT2,TCN1, DSE, CCR3, CRIP1, ITK, KLRF1, TGFBR1, GSR, HIST1H4E, HPGD, FRMD3,ABCA13, C11orf82, PPP2R5A, BPI, CASS4, AP3B2, ODZ1, TMTC1, ADM, FGFBP2,HSPC159, HLA-DRA, HIST1H3I, TMEM144, MRPL41, FOLR3, PICALM, SH3PXD2B,DDAH2, HLA-DPB1, KPNA5, PHOSPHO1, TPST1, EIF2AK2, OR9A2, OLFM4, CD163,CDA, CHI3L1, MTHFS, CLU, ANAPC11, JUP, PMAIP1, GIMAP7, KLRD1, CCR1,CD274, EFCAB2, SUCNR1, KCNMA1, LGALS2, SLC11A1, FOXD4L3, VAMP2, ITGA4,LHFP, PRR13, FFAR2, B3GAT3, EAF2, HPSE, CLC, TLR10, CCR4, HIST1H3A,CENPK, DPH3, HLA-DPA1, ATP13A3, DNAJC9, S100B, HIST1H3J, 110, RPL17,C15orf54, LRRC70, IL5RA, PLA2G7, ECHDC3, HINT1, LCN2, PPIF, SLC15A2,PMS2CL, HIST1H2AA, CEACAM8, HSP90AB1, ABCG1, PDGFC, NPCDR1, PDK4, GAB2,WSB2, FAM118A, JKAMP, TREML1, PYHIN1, IRF4, ABCA1, DAAM2, ACPL2, RCBTB2,SAP30, THBS1, PCOLCE2, GPR65, NF-E4, LTF, LASS4, B4GALT3, RETN, TIMM10,IL1B, CLEC4A, SEC24A, RUNX2, LRRFIP1, CFD, EIF1AX, ZRANB1, SULF2,EXOSC4, CCDC125, LOC284757, ANKRD28, HIST1H2AJ, CD63, PLIN2, SON,HIST1H4L, KRTAP15-1, DLEU2, MYL9, FABP2, CD24, MACF1, GSTO1, RRP12,AIG1, RASA4, FBXL13, PDE3B, CCRL2, C1orf128, E2F6, IL1RL1, CEACAM6,CYP4F3, 199, TAAR1, TSHZ2, PLB1, UBE2F; (2) obtaining a sample IRSbiomarker profile from the subject, which evaluates for an individualIRS biomarker in the reference IRS biomarker profile a corresponding IRSbiomarker, and (3) determining a likelihood of the subject having or nothaving the healthy condition or SIRS based on the sample IRS biomarkerprofile and the reference IRS biomarker profile.

Suitably, the method determines the likelihood that inSIRS, ipSIRS or ahealthy condition is present or absent in the subject, wherein themethod comprises: 1) providing a correlation of a reference IRSbiomarker profile with the likelihood of having or not having inSIRS,ipSIRS or the healthy condition, wherein the reference biomarker profileevaluates at least one IRS biomarker selected from PLACE, 132, INSIG1,CDS2, VOPP1, SLC39A9, B3GAT3, CD300A, OCR1, PTGER2, LGALS1, HIST1H4L,AMFR, SIAE, SLC39A8, TGFBR1, GAB2, MRPL41, TYMS, HIST1H3B, MPZL3,KIAA1257, OMG, HIST1H2BM, TDRD9, C22orf37, GALNT3, SYNE2, MGST3,HIST1H3I, LOC284757, TRAF3, HIST1H3C, STOM, C3AR1, KIAA0101, TNFRSF17,HAL, UBE2J1, GLT25D1, CD151, HSPB1, IMP3, PICALM, ACER3, IGL@,HIST1H2BJ, CASS4, KREMEN1, IRS2, APOLD1, RBP7, DNAJC13, ERGIC1, FSD1L,TLR5, TMEM62, SDHC, C9orf72, NP, KIAA0746, PMAIP1, DSE, SMPDL3A, DNAJC9,HIST1H3H, CDC26, CRIP1, FAR2, FRMD3, RGS2, METTL7B, CLEC4E, MME, ABCA13,PRR13, HIST1H4C, RRP12, GLDC, ECHDC3, IRF1, C7orf53, IGK@, RNASE2,FCGR1A, SAP30, PMS2CL, SLC11A1, AREG, PLB1, PPIF, GSR, NFXL1, AP3B2,DCTN5, RPL17, IGLV6-57, KLRF1, CHI3L1, ANKRD34B, OLFM4, CPM, CCDC125,GPR56, PPP1R2, 110, ACPL2, HIST1H3A, C7orf58, IRF4, ANAPC11, HIST1H3J,KLRD1, GPR84, ZRANB1, KDM6B, TPST1, HINT1, DAAM2, PTGDR, FKBP5,HSP90AB1, HPGD, IF116, CD177, TAS2R31, CD163, B4GALT3, EIF1AX, CYP4F3,HIST1H2AA, LASS4 (where if a gene name is not provided then a SEQ ID NO.is provided).; (2) obtaining a sample IRS biomarker profile from thesubject, which evaluates for an individual IRS biomarker in thereference IRS biomarker profile a corresponding IRS biomarker; and (3)determining a likelihood of the subject having or not having inSIRS,ipSIRS or a healthy condition the condition based on the sample IRSbiomarker profile and the reference IRS biomarker profile.

In some embodiments, the method determines the likelihood that inSIRS oripSIRS is present or absent in the subject, wherein the methodcomprises: 1) providing a correlation of a reference IRS biomarkerprofile with the likelihood of having or not having inSIRS or ipSIRS,wherein the reference biomarker profile evaluates at least one IRSbiomarker selected from C11orf82, PLACE, 132, INSIG1, CDS2, VOPP1,SLC39A9, FOXD4L3, WSB2, CD63, CD274, B3GAT3, CD300A, OCR1, JKAMP, TLR10,PTGER2, PDGFC, LGALS1, HIST1H4L, AGTRAP, AMFR, SIAE, 200, SLC15A2,SLC39A8, TGFBR1, DDAH2, HPSE, SUCNR1, MTRR, GAB2, P4HA1, HS2ST1, MRPL41,TYMS, RUNX2, GSTO1, LRRC70, HIST1H3B, RCBTB2, MPZL3, KIAA1257, AIG1,NEK6, OMG, HIST1H2BM, TDRD9, GALNT3, ATP13A3, C22orf37, SYNE2, ADM,MGST3, PDE3B, HIST1H3I, LOC284757, TRAF3, HIST1H3C, STOM, KLHL5, EXOSC4,C3AR1, KIAA0101, TNFRSF17, HAL, UBE2J1, GLT25D1, CD151, TPX2, PCOLCE2,HSPB1, EAF2, IMP3, PICALM, ACER3, IGL@, HIST1H2BJ, CASS4, ACTA2, PTGS1,KREMEN1, IRS2, TAF13, FSD1L, APOLD1, RBP7, DNAJC13, SEC24A, ERGIC1,FSD1L, TLR5, MKI67, TMEM62, CLEC4A, SDHC, C9orf72, NP, CLU, ABCA1,KIAA0746, PMAIP1, DSE, CMTM5, SMPDL3A, DNAJC9, HDHD1A, HIST1H3H, CDC26,ICAM1, LOC100128751, FAR2, CRIP1, MPZL2, FRMD3, CTSL1, METTL7B, RGS2,CLEC4E, MME, ABCA13, PRR13, HIST1H4C, RRP12, GLDC, ECHDC3, ITGA2B,C7orf53, IRF1, 268, IGK@, RNASE2, FCGR1A, UBE2F, SAP30, LAIR1, PMS2CL,SLC11A1, PLB1, AREG, PPIF, GSR, NFXL1, AP3B2, DCTN5, RPL17, PLA2G7,GALNT2, IGLV6-57, KLRF1, CHI3L1, ANKRD34B, OLFM4, 199, CPM, CCDC125,SULF2, LTF, GPR56, MACF1, PPP1R2, DYNLL1, LCN2, FFAR2, SFRS9, IGJ,FAM118B, 110, ACPL2, HIST1H3A, C7orf58, ANAPC11, HIST1H3J, IRF4, MPO,TREML1, KLRD1, GPR84, CCRL2, CAMK1D, CCR1, ZRANB1, KDM6B, TPST1, HINT1,DAAM2, PTGDR, FKBP5, CD24, HSP90AB1, HPGD, CEACAM8, DEFA4, IL1B, IF116,CD177, KIAA1324, SRXN1, TAS2R31, CEACAM6, CD163, B4GALT3, ANKRD28,TAAR1, EIF1AX, CYP4F3, 314, HIST1H2AA, LY6G5B, LASS4 (where if a genename is not provided then a SEQ ID NO. is provided); (2) obtaining asample IRS biomarker profile from the subject, which evaluates for anindividual IRS biomarker in the reference IRS biomarker profile acorresponding IRS biomarker; and (3) determining a likelihood of thesubject having or not having inSIRS or ipSIRS based on the sample IRSbiomarker profile and the reference IRS biomarker profile.

Suitably, the method determines the likelihood that a stage of ipSIRSselected from mild sepsis, severe sepsis and septic shock is present orabsent the subject, wherein the method comprises: 1) providing acorrelation of a reference IRS biomarker profile with the likelihood ofhaving or not having the stage of ipSIRS, wherein the referencebiomarker IRS biomarker profile evaluates at least one IRS biomarkerselected from PLEKHA3, PLEKHF2, 232, SFRS9, ZNF587, KPNA5, LOC284757,GPR65, VAMP2, SLC1A3, ITK, ATF7, ZNF28, AIF1, MINPP1, GIMAP7, MKI67,IRF4, TSHZ2, HLA-DPB1, EFCAB2, POLE2, FAIM3, 110, CAMK4, TRIM21, IF144,CENPK, ATP5L, GPR56, HLA-DPA1, C4orf3, GSR, GNLY, RFESD, BPI, HIST1H2AA,NF-E4, CALM2, EIF1AX, E2F6, ARL17P1, TLR5, SH3PXD2B, FAM118A, RETN,PMAIP1, DNAJC9, PCOLCE2, TPX2, BMX, LRRFIP1, DLEU2, JKAMP, JUP, ABCG1,SLC39A9, B3GNT5, ACER3, LRRC70, NPCDR1, TYMS, HLA-DRA, TDRD9, FSD1L,FAR2, C7orf53, PPP1R2, SGMS2, EXOSC4, TGFBR1, CD24, TCN1, TAF13, AP3B2,CD63, SLC15A2, IL18R1, ATP6V0D1, SON, HSP90AB1, CEACAM8, SMPDL3A, IMP3,SEC24A, PICALM, 199, CEACAM6, CYP4F3, OLAH, ECHDC3, ODZ1, KIAA0746,KIAA1324, HINT1, VNN1, C22orf37, FSD1L, FOLR3, IL1RL1, OMG, MTHFS,OLFM4, S100B, ITGA4, KLRD1, SLC39A8, KLHL5, KLRK1, MPO, PPIF, GOT2,LRRN3, HIST1H2AJ, CLU, LCN2, 132, CEP97, KLRF1, FBXL13, HIST1H3B,ANKRD34B, RPIA, HPGD, HIST2H2BF, GK3P (where if a gene name is notprovided then a SEQ ID NO. is provided).; (2) obtaining a sample IRSbiomarker profile from the subject, which evaluates for an individualIRS biomarker in the reference IRS biomarker profile a corresponding IRSbiomarker; and (3) determining a likelihood of the subject having or nothaving the stage of ipSIRS based on the sample IRS biomarker profile andthe reference IRS biomarker profile.

In illustrative examples, an individual IRS biomarker is selected fromthe group consisting of: (a) a polynucleotide expression productcomprising a nucleotide sequence that shares at least 70% (or at least71% to at least 99% and all integer percentages in between) sequenceidentity with the sequence set forth in any one of SEQ ID NO: 1-319, ora complement thereof; (b) a polynucleotide expression product comprisinga nucleotide sequence that encodes a polypeptide comprising the aminoacid sequence set forth in any one of SEQ ID NO: 320-619; (c) apolynucleotide expression product comprising a nucleotide sequence thatencodes a polypeptide that shares at least 70% (or at least 71% to atleast 99% and all integer percentages in between) sequence similarity oridentity with at least a portion of the sequence set forth in SEQ ID NO:320-619; (d) a polynucleotide expression product comprising a nucleotidesequence that hybridizes to the sequence of (a), (b), (c) or acomplement thereof, under medium or high stringency conditions; (e) apolypeptide expression product comprising the amino acid sequence setforth in any one of SEQ ID NO: 320-619; and (f) a polypeptide expressionproduct comprising an amino acid sequence that shares at least 70% (orat least 71% to at least 99% and all integer percentages in between)sequence similarity or identity with the sequence set forth in any oneof SEQ ID NO: 320-619.

Evaluation of IRS markers suitably includes determining the levels ofindividual IRS markers, which correlate with the presence or absence ofa condition, as defined above.

In some embodiments, the method of determining the likelihood of thepresence or absence of a condition, as broadly described above,comprises comparing the level of a first IRS biomarker in the sample IRSbiomarker profile with the level of a second IRS biomarker in the sampleIRS biomarker profile to provide a ratio and determining a likelihood ofthe presence or absence of the condition based on that ratio. Inillustrative examples of this type, the determination is carried out inthe absence of comparing the level of the first or second IRS biomarkersin the sample IRS biomarker profile to the level of a corresponding IRSbiomarker in the reference IRS biomarker profile. Representative IRSbiomarkers that are useful for these embodiments are suitably selectedfrom those listed in Example 6 and Tables 16-21.

In a related aspect, the present invention provides a kit comprising oneor more reagents and/or devices for use in performing the method ofdetermining the likelihood of the presence or absence of a condition asbroadly described above.

Another aspect of the present invention provides a method for treating,preventing or inhibiting the development of inSIRS, ipSIRS or aparticular stage of ipSIRS in a subject, the method comprising: (1)correlating a reference IRS biomarker profile with the presence orabsence of a condition selected from a healthy condition, SIRS, inSIRS,ipSIRS, or a particular stage of ipSIRS, wherein the reference IRSbiomarker profile evaluates at least one IRS biomarker; (2) obtaining anIRS biomarker profile of a sample from a subject, wherein the sample IRSbiomarker profile evaluates for an individual IRS biomarker in thereference IRS biomarker profile a corresponding IRS biomarker; (3)determining a likelihood of the subject having or not having thecondition based on the sample IRS biomarker profile and the referenceIRS biomarker profile, and administering to the subject, on the basisthat the subject has an increased likelihood of having inSIRS, aneffective amount of an agent that treats or ameliorates the symptoms orreverses or inhibits the development of inSIRS, or administering to thesubject, on the basis that the subject has an increased likelihood ofhaving ipSIRS or a particular stage of ipSIRS, an effective amount of anagent that treats or ameliorates the symptoms or reverses or inhibitsthe development of ipSIRS or the particular stage of ipSIRS.

Yet another aspect of the present invention provides a method ofmonitoring the efficacy of a particular treatment regimen in a subjecttowards a desired health state (e.g., healthy condition), the methodcomprising: (1) providing a correlation of a reference IRS biomarkerprofile with the likelihood of having a healthy condition; (2) obtaininga corresponding IRS biomarker profile of a subject having inSIRS, ipSIRSor a particular stage of ipSIRS after treatment with a treatmentregimen, wherein a similarity of the subject's IRS biomarker profileafter treatment to the reference IRS biomarker profile indicates thelikelihood that the treatment regimen is effective for changing thehealth status of the subject to the desired health state.

Still another aspect of the present invention provides a method ofcorrelating a reference IRS biomarker profile with an effectivetreatment regimen for a condition selected from inSIRS, ipSIRS or aparticular stage of ipSIRS, wherein the reference IRS biomarker profileevaluates at least one IRS biomarker, the method comprising: (a)determining a sample IRS biomarker profile from a subject with thecondition prior to treatment, wherein the sample IRS biomarker profileevaluates for an individual IRS biomarker in the reference IRS biomarkerprofile a corresponding IRS biomarker; and correlating the sample IRSbiomarker profile with a treatment regimen that is effective fortreating the condition.

In another aspect, the present invention provides a method ofdetermining whether a treatment regimen is effective for treating asubject with a condition selected from inSIRS, ipSIRS or a particularstage of ipSIRS, the method comprising: (a) correlating a referencebiomarker profile prior to treatment with an effective treatment regimenfor the condition, wherein the reference IRS biomarker profile evaluatesat least one IRS biomarker; and (b) obtaining a sample IRS biomarkerprofile from the subject after treatment, wherein the sample IRSbiomarker profile evaluates for an individual IRS biomarker in thereference IRS biomarker profile a corresponding IRS biomarker, andwherein the sample IRS biomarker profile after treatment indicateswhether the treatment regimen is effective for treating the condition inthe subject.

In a further aspect, the present invention provides a method ofcorrelating an IRS biomarker profile with a positive or negativeresponse to a treatment regimen, the method comprising: (a) obtaining anIRS biomarker profile from a subject with a condition selected frominSIRS, ipSIRS or a particular stage of ipSIRS following commencement ofthe treatment regimen, wherein the IRS biomarker profile evaluates atleast one IRS biomarker; and (b) correlating the IRS biomarker profilefrom the subject with a positive or negative response to the treatmentregimen.

Another aspect of the present invention provides a method of determininga positive or negative response to a treatment regimen by a subject witha condition selected from inSIRS, ipSIRS or a particular stage ofipSIRS, the method comprising: (a) correlating a reference IRS biomarkerprofile with a positive or negative response to the treatment regimen,wherein the reference IRS biomarker profile evaluates at least one(e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, etc.) IRS biomarker; and (b)determining a sample IRS biomarker profile from the subject, wherein thesubject's sample IRS biomarker profile evaluates for an individual IRSbiomarker in the reference IRS biomarker profile a corresponding IRSbiomarker and indicates whether the subject is responding to thetreatment regimen.

In some embodiments, the method of determining a positive or negativeresponse to a treatment regimen further comprises: determining a firstsample IRS biomarker profile from the subject prior to commencing thetreatment regimen, wherein the first sample IRS biomarker profileevaluates at least one IRS biomarker; and comparing the first sample IRSbiomarker profile with a second sample IRS biomarker profile from thesubject after commencement of the treatment regimen, wherein the secondsample IRS biomarker profile evaluates for an individual IRS biomarkerin the first sample IRS biomarker profile a corresponding IRS biomarker.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a box and whiskers plot of PLEKHA3 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 2 shows a box and whiskers plot of VAMP2 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 3 shows a box and whiskers plot of ITK gene expression for healthysubjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 4 shows a box and whiskers plot of C11orf82 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 5 shows a box and whiskers plot of PLAC8 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 6 shows a box and whiskers plot of INSIG1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 7 shows a box and whiskers plot of FCGR1A//FCGR1B//FCGR1C geneexpression for healthy subjects and subjects with SIRS, mild sepsis,severe sepsis.

FIG. 8 shows a box and whiskers plot of CHI3L1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 9 shows a box and whiskers plot of CD177 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 10 shows a box and whiskers plot of GNLY gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 11 shows a box and whiskers plot of BMX//HNRPDL gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 12 shows a box and whiskers plot of TLR5 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 13 shows a box and whiskers plot of TLR5 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 14 shows a box and whiskers plot of CD177 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 15 shows a box and whiskers plot of VNN1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 16 shows a box and whiskers plot of UBE2J1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 17 shows a box and whiskers plot of IMP3 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 18 shows a box and whiskers plot of RNASE2//LOC643332 geneexpression for healthy subjects and subjects with SIRS, mild sepsis,severe sepsis.

FIG. 19 shows a box and whiskers plot of CLEC4D gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 20 shows a box and whiskers plot of C3AR1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 21 shows a box and whiskers plot of GPR56 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 22 shows a box and whiskers plot of ARG1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 23 shows a box and whiskers plot of FCGR1A//FCGR1B//FCGR1C geneexpression for healthy subjects and subjects with SIRS, mild sepsis,severe sepsis.

FIG. 24 shows a box and whiskers plot of C11orf82 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 25 shows a box and whiskers plot of FAR2 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 26 shows a box and whiskers plot of GNLY gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 27 shows a box and whiskers plot of GALNT3 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 28 shows a box and whiskers plot of OMG gene expression for healthysubjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 29 shows a box and whiskers plot of SLC37A3 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 30 shows a box and whiskers plot of BMX//HNRPDL gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 31 shows a box and whiskers plot of STOM gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 32 shows a box and whiskers plot of TDRD9 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 33 shows a box and whiskers plot of KREMEN1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 34 shows a box and whiskers plot of FAIM3 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 35 shows a box and whiskers plot of CLEC4E gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 36 shows a box and whiskers plot of IL18R1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 37 shows a box and whiskers plot of ACER3 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 38 shows a box and whiskers plot of ERLIN1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 39 shows a box and whiskers plot of TGFBR1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 40 shows a box and whiskers plot of FKBP5//LOC285847 geneexpression for healthy subjects and subjects with SIRS, mild sepsis,severe sepsis.

FIG. 41 shows a box and whiskers plot of GPR84 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 42 shows a box and whiskers plot of C7orf53 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 43 shows a box and whiskers plot of PLB1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 44 shows a box and whiskers plot of DSE gene expression for healthysubjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 45 shows a box and whiskers plot of PTGDR gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 46 shows a box and whiskers plot of CAMK4 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 47 shows a box and whiskers plot of DNAJC13 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 48 shows a box and whiskers plot of TNFAIP6 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 49 shows a box and whiskers plot ofFOXD4L3//FOXD4L6//FOXD4//FOXD4L1//FOXD4L2//FOXD4L4//FOXD4L5 geneexpression for healthy subjects and subjects with SIRS, mild sepsis,severe sepsis.

FIG. 50 shows a box and whiskers plot of MMP9//LOC100128028 geneexpression for healthy subjects and subjects with SIRS, mild sepsis,severe sepsis.

FIG. 51 shows a box and whiskers plot of GSR gene expression for healthysubjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 52 shows a box and whiskers plot of KLRF1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 53 shows a box and whiskers plot of SH2D1B gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 54 shows a box and whiskers plot of ANKRD34B gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 55 shows a box and whiskers plot of SGMS2 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 56 shows a box and whiskers plot of B3GNT5//MCF2L2 gene expressionfor healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 57 shows a box and whiskers plot of GK3P//GK gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 58 shows a box and whiskers plot of PFKFB2 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 59 shows a box and whiskers plot of PICALM gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 60 shows a box and whiskers plot of METTL7B gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 61 shows a box and whiskers plot of HIST1H4C gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 62 shows a box and whiskers plot of C9orf72 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 63 shows a box and whiskers plot of HIST1H3I gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 64 shows a box and whiskers plot of SLC15A2 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 65 shows a box and whiskers plot of TLR10 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 66 shows a box and whiskers plot of ADM gene expression for healthysubjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 67 shows a box and whiskers plot of CD274 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 68 shows a box and whiskers plot of CRIP1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 69 shows a box and whiskers plot of LRRN3 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 70 shows a box and whiskers plot of HLA-DPB1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 71 shows a box and whiskers plot of VAMP2 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 72 shows a box and whiskers plot of SMPDL3A gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 73 shows a box and whiskers plot of IFI16 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 74 shows a box and whiskers plot of JKAMP gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 75 shows a box and whiskers plot of MRPL41 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 76 shows a box and whiskers plot of SLC1A3 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 77 shows a box and whiskers plot of OLFM4 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 78 shows a box and whiskers plot of CASS4 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 79 shows a box and whiskers plot of TCN1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 80 shows a box and whiskers plot of WSB2 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 81 shows a box and whiskers plot of CLU gene expression for healthysubjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 82 shows a box and whiskers plot of ODZ1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 83 shows a box and whiskers plot of KPNA5 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 84 shows a box and whiskers plot of PLAC8 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 85 shows a box and whiskers plot of CD63 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 86 shows a box and whiskers plot of HPSE gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 87 shows a box and whiskers plot of C1orf161 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 88 shows a box and whiskers plot of DDAH2 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 89 shows a box and whiskers plot of KLRK1//KLRC4 gene expressionfor healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 90 shows a box and whiskers plot of ATP13A3 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 91 shows a box and whiskers plot of ITK gene expression for healthysubjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 92 shows a box and whiskers plot of PMAIP1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 93 shows a box and whiskers plot of LOC284757 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 94 shows a box and whiskers plot of GOT2 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 95 shows a box and whiskers plot of PDGFC gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 96 shows a box and whiskers plot of B3GAT3 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 97 shows a box and whiskers plot of HIST1H4E gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 98 shows a box and whiskers plot of HPGD gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 99 shows a box and whiskers plot of FGFBP2 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 100 shows a box and whiskers plot of LRRC70//IPO11 gene expressionfor healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 101 shows a box and whiskers plot of TMEM144/LOC285505 geneexpression for healthy subjects and subjects with SIRS, mild sepsis,severe sepsis.

FIG. 102 shows a box and whiskers plot of CDS2 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 103 shows a box and whiskers plot of BPI gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 104 shows a box and whiskers plot of ECHDC3 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 105 shows a box and whiskers plot of CCR3 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 106 shows a box and whiskers plot of HSPC159 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 107 shows a box and whiskers plot of OLAH gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 108 shows a box and whiskers plot of PPP2R5A//SNORA16B geneexpression for healthy subjects and subjects with SIRS, mild sepsis,severe sepsis.

FIG. 109 shows a box and whiskers plot of TMTC1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 110 shows a box and whiskers plot of EAF2//HCG11//LOC647979 geneexpression for healthy subjects and subjects with SIRS, mild sepsis,severe sepsis.

FIG. 111 shows a box and whiskers plot of RCBTB2//LOC100131993 geneexpression for healthy subjects and subjects with SIRS, mild sepsis,severe sepsis.

FIG. 112 shows a box and whiskers plot of SEC24A//SAR1B gene expressionfor healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 113 shows a box and whiskers plot of SH3PXD2B gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 114 shows a box and whiskers plot of HMGB2 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 115 shows a box and whiskers plot of KLRD1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 116 shows a box and whiskers plot of CHI3L1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 117 shows a box and whiskers plot of FRMD3 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 118 shows a box and whiskers plot of SLC39A9 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 119 shows a box and whiskers plot of GIMAP7 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 120 shows a box and whiskers plot of ANAPC11 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 121 shows a box and whiskers plot of EXOSC4 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 122 shows a box and whiskers plot of NA gene expression for healthysubjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 123 shows a box and whiskers plot of INSIG1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 124 shows a box and whiskers plot of FOLR3//FOLR2 gene expressionfor healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 125 shows a box and whiskers plot of RUNX2 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 126 shows a box and whiskers plot of PRR13//PCBP2 gene expressionfor healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 127 shows a box and whiskers plot of HIST1H4L gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 128 shows a box and whiskers plot of LGALS1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 129 shows a box and whiskers plot of CCR1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 130 shows a box and whiskers plot of TPST1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 131 shows a box and whiskers plot of HLA-DRA gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 132 shows a box and whiskers plot of CD163 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 133 shows a box and whiskers plot of FFAR2 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 134 shows a box and whiskers plot of PHOSPHO1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 135 shows a box and whiskers plot of PPIF gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 136 shows a box and whiskers plot of MTHFS gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 137 shows a box and whiskers plot of DNAJC9//FAM149B1//RPL26 geneexpression for healthy subjects and subjects with SIRS, mild sepsis,severe sepsis.

FIG. 138 shows a box and whiskers plot of LCN2 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 139 shows a box and whiskers plot of EIF2AK2 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 140 shows a box and whiskers plot of LGALS2 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 141 shows a box and whiskers plot of SIAE gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 142 shows a box and whiskers plot of AP3B2 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 143 shows a box and whiskers plot of ABCA13 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 144 shows a box and whiskers plot of NA expression for healthysubjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 145 shows a box and whiskers plot of EFCAB2 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 146 shows a box and whiskers plot of HIST1H2AA gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 147 shows a box and whiskers plot of HINT1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 148 shows a box and whiskers plot of HIST1H3J gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 149 shows a box and whiskers plot of CDA gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 150 shows a box and whiskers plot of SAP30 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 151 shows a box and whiskers plot of AGTRAP gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 152 shows a box and whiskers plot of SUCNR1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 153 shows a box and whiskers plot of MTRR gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 154 shows a box and whiskers plot of PLA2G7 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 155 shows a box and whiskers plot of AIG1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 156 shows a box and whiskers plot of PCOLCE2 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 157 shows a box and whiskers plot of GAB2 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 158 shows a box and whiskers plot of HS2ST1//UBA2 gene expressionfor healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 159 shows a box and whiskers plot of HIST1H3A gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 160 shows a box and whiskers plot of C22orf37 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 161 shows a box and whiskers plot of HLA-DPA1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 162 shows a box and whiskers plot of VOPP1//LOC100128019 geneexpression for healthy subjects and subjects with SIRS, mild sepsis,severe sepsis.

FIG. 163 shows a box and whiskers plot of SLC39A8 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 164 shows a box and whiskers plot of MKI67 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 165 shows a box and whiskers plot of SLC11A1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 166 shows a box and whiskers plot of AREG gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 167 shows a box and whiskers plot of ABCA1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 168 shows a box and whiskers plot of DAAM2//LOC100131657 geneexpression for healthy subjects and subjects with SIRS, mild sepsis,severe sepsis.

FIG. 169 shows a box and whiskers plot of LTF gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 170 shows a box and whiskers plot of TREML1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 171 shows a box and whiskers plot of GSTO1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 172 shows a box and whiskers plot of PTGER2 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 173 shows a box and whiskers plot of CEACAM8 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 174 shows a box and whiskers plot of CLEC4A gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 175 shows a box and whiskers plot of PMS2CL/PMS2 gene expressionfor healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 176 shows a box and whiskers plot of RETN gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 177 shows a box and whiskers plot of PDE3B gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 178 shows a box and whiskers plot of SULF2 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 179 shows a box and whiskers plot of NEK6//LOC100129034 geneexpression for healthy subjects and subjects with SIRS, mild sepsis,severe sepsis.

FIG. 180 shows a box and whiskers plot of CENPK gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 181 shows a box and whiskers plot of TRAF3 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 182 shows a box and whiskers plot of GPR65 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 183 shows a box and whiskers plot of IRF4 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 184 shows a box and whiskers plot of MACF1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 185 shows a box and whiskers plot of AMFR gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 186 shows a box and whiskers plot of RPL17//SNORD58B geneexpression for healthy subjects and subjects with SIRS, mild sepsis,severe sepsis.

FIG. 187 shows a box and whiskers plot of IRS2 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 188 shows a box and whiskers plot of JUP gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 189 shows a box and whiskers plot of CD24 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 190 shows a box and whiskers plot of GALNT2 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 191 shows a box and whiskers plot of HSP90AB1//HSP90AB3P//HSP90AB2Pgene expression for healthy subjects and subjects with SIRS, mildsepsis, severe sepsis.

FIG. 192 shows a box and whiskers plot of GLT25D1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 193 shows a box and whiskers plot of OR9A2 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 194 shows a box and whiskers plot of HDHD1A gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 195 shows a box and whiskers plot of ACTA2 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 196 shows a box and whiskers plot of ACPL2 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 197 shows a box and whiskers plot of LRRFIP1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 198 shows a box and whiskers plot of KCNMA1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 199 shows a box and whiskers plot of OCR1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 200 shows a box and whiskers plot of ITGA4//CERKL gene expressionfor healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 201 shows a box and whiskers plot of EIF1AX//SCARNA9L//EIF1AP1 geneexpression for healthy subjects and subjects with SIRS, mild sepsis,severe sepsis.

FIG. 202 shows a box and whiskers plot of SFRS9 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 203 shows a box and whiskers plot of DPH3 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 204 shows a box and whiskers plot of ERGIC1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 205 shows a box and whiskers plot of CD300A gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 206 shows a box and whiskers plot of NF-E4 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 207 shows a box and whiskers plot of MINPP1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 208 shows a box and whiskers plot of TRIM21 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 209 shows a box and whiskers plot of ZNF28 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 210 shows a box and whiskers plot of NPCDR1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 211 shows a box and whiskers plot of NA gene expression for healthysubjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 212 shows a box and whiskers plot of NA gene expression for healthysubjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 213 shows a box and whiskers plot of ICAM1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 214 shows a box and whiskers plot of TAF13 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 215 shows a box and whiskers plot of P4HA1//RPL17 gene expressionfor healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 216 shows a box and whiskers plot of C15orf54 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 217 shows a box and whiskers plot of KLHL5 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 218 shows a box and whiskers plot of HAL gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 219 shows a box and whiskers plot of DLEU2//DLEU2L gene expressionfor healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 220 shows a box and whiskers plot of ANKRD28 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 221 shows a box and whiskers plot of LY6G5B//CSNK2B gene expressionfor healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 222 shows a box and whiskers plot of KIAA1257//ACAD9//LOC100132731gene expression for healthy subjects and subjects with SIRS, mildsepsis, severe sepsis.

FIG. 223 shows a box and whiskers plot of MGST3 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 224 shows a box and whiskers plot of KIAA0746 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 225 shows a box and whiskers plot of HSPB1//HSPBL2 gene expressionfor healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 226 shows a box and whiskers plot of CCR4 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 227 shows a box and whiskers plot of TYMS gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 228 shows a box and whiskers plot of RRP12//LOC644215 geneexpression for healthy subjects and subjects with SIRS, mild sepsis,severe sepsis.

FIG. 229 shows a box and whiskers plot of CCDC125 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 230 shows a box and whiskers plot of HIST1H2BM gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 231 shows a box and whiskers plot of PDK4 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 232 shows a box and whiskers plot of ABCG1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 233 shows a box and whiskers plot of IL1B gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 234 shows a box and whiskers plot of THBS1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 235 shows a box and whiskers plot of ITGA2B gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 236 shows a box and whiskers plot of LHFP gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 237 shows a box and whiskers plot of LAIR1//LAIR2 gene expressionfor healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 238 shows a box and whiskers plot of HIST1H3B gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 239 shows a box and whiskers plot of ZRANB1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 240 shows a box and whiskers plot of TIMM10 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 241 shows a box and whiskers plot of FSD1L//GARNL1 gene expressionfor healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 242 shows a box and whiskers plot of HIST1H2AJ//HIST1H2AI geneexpression for healthy subjects and subjects with SIRS, mild sepsis,severe sepsis.

FIG. 243 shows a box and whiskers plot of PTGS1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 244 shows a box and whiskers plot of NA gene expression for healthysubjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 245 shows a box and whiskers plot of UBE2F//C20orf194//SCLY geneexpression for healthy subjects and subjects with SIRS, mild sepsis,severe sepsis.

FIG. 246 shows a box and whiskers plot of HIST1H3C gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 247 shows a box and whiskers plot of FAM118A gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 248 shows a box and whiskers plot of CCRL2 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 249 shows a box and whiskers plot of E2F6 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 250 shows a box and whiskers plot of MPZL3 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 251 shows a box and whiskers plot of SRXN1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 252 shows a box and whiskers plot of CD151 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 253 shows a box and whiskers plot of HIST1H3H gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 254 shows a box and whiskers plot of FSD1L gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 255 shows a box and whiskers plot of RFESD//SPATA9 gene expressionfor healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 256 shows a box and whiskers plot of TPX2 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 257 shows a box and whiskers plot of S100B gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 258 shows a box and whiskers plot of ZNF587//ZNF417 gene expressionfor healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 259 shows a box and whiskers plot of PYHIN1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 260 shows a box and whiskers plot of KIAA1324 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 261 shows a box and whiskers plot of CEACAM6//CEACAM5 geneexpression for healthy subjects and subjects with SIRS, mild sepsis,severe sepsis.

FIG. 262 shows a box and whiskers plot of APOLD1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 263 shows a box and whiskers plot of FABP2 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 264 shows a box and whiskers plot of KDM6B//TMEM88 gene expressionfor healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 265 shows a box and whiskers plot ofIGKV3-20//IGKV3D-15//LOC440871//LOC652493//LOC100291464//LOC652694//IGKV3-15gene expression for healthy subjects and subjects with SIRS, mildsepsis, severe sepsis.

FIG. 266 shows a box and whiskers plot of MYL9 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 267 shows a box and whiskers plot of HIST1H2BJ gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 268 shows a box and whiskers plot of TAAR1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 269 shows a box and whiskers plot of CLC gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 270 shows a box and whiskers plot of CYP4F3//CYP4F2 gene expressionfor healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 271 shows a box and whiskers plot of CEP97 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 272 shows a box and whiskers plot of SON gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 273 shows a box and whiskers plot of IRF1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 274 shows a box and whiskers plot of SYNE2 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 275 shows a box and whiskers plot of MME gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 276 shows a box and whiskers plot of LASS4 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 277 shows a box and whiskers plot of DEFA4//DEFA8P gene expressionfor healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 278 shows a box and whiskers plot of C7orf58 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 279 shows a box and whiskers plot of DYNLL1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 280 shows a box and whiskers plot of NA gene expression for healthysubjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 281 shows a box and whiskers plot of MPO gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 282 shows a box and whiskers plot of CPM gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 283 shows a box and whiskers plot of TSHZ2 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 284 shows a box and whiskers plot of PLIN2 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 285 shows a box and whiskers plot of FAM118B gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 286 shows a box and whiskers plot of B4GALT3 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 287 shows a box and whiskers plot ofRASA4//RASA4PHRASA4B//POLR2J4//LOC100132214 gene expression for healthysubjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 288 shows a box and whiskers plot of CTSL1//CTSLL3 gene expressionfor healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 289 shows a box and whiskers plot of NP gene expression for healthysubjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 290 shows a box and whiskers plot of ATF7 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 291 shows a box and whiskers plot of SPARC gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 292 shows a box and whiskers plot of PLB1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 293 shows a box and whiskers plot of C4orf3 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 294 shows a box and whiskers plot of POLE2 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 295 shows a box and whiskers plot of TNFRSF17 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 296 shows a box and whiskers plot of FBXL13 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 297 shows a box and whiskers plot of PLEKHA3 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 298 shows a box and whiskers plot of TMEM62//SPCS2//LOC653566 geneexpression for healthy subjects and subjects with SIRS, mild sepsis,severe sepsis.

FIG. 299 shows a box and whiskers plot of RBP7 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 300 shows a box and whiskers plot of PLEKHF2 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 301 shows a box and whiskers plot of RGS2 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 302 shows a box and whiskers plot of ATP6V0D1//LOC100132855 geneexpression for healthy subjects and subjects with SIRS, mild sepsis,severe sepsis.

FIG. 303 shows a box and whiskers plot of RPIA gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 304 shows a box and whiskers plot of CAMK1D gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 305 shows a box and whiskers plot of IL1RL1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 306 shows a box and whiskers plot of CMTM5 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 307 shows a box and whiskers plot of AIF1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 308 shows a box and whiskers plot of CFD gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 309 shows a box and whiskers plot of MPZL2 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 310 shows a box and whiskers plot of LOC100128751 gene expressionfor healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 311 shows a box and whiskers plot of IGJ gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 312 shows a box and whiskers plot of CDC26 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 313 shows a box and whiskers plot of PPP1R2//PPP1R2P3 geneexpression for healthy subjects and subjects with SIRS, mild sepsis,severe sepsis.

FIG. 314 shows a box and whiskers plot of IL5RA gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 315 shows a box and whiskers plot of ARL17P1//ARL17 gene expressionfor healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 316 shows a box and whiskers plot of ATP5L//ATP5L2 gene expressionfor healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 317 shows a box and whiskers plot of TAS2R31 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 318 shows a box and whiskers plot of HIST2H2BF//HIST2H3D geneexpression for healthy subjects and subjects with SIRS, mild sepsis,severe sepsis.

FIG. 319 shows a box and whiskers plot of CALM2//C2orf61 gene expressionfor healthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 320 shows a box and whiskers plot of SPATA6 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 321 shows a box and whiskers plot of IGLV6-57 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 322 shows a box and whiskers plot of C1orf128 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 323 shows a box and whiskers plot of KRTAP15-1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 324 shows a box and whiskers plot of IFI44 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 325 shows a box and whiskers plot ofIGLV3-25//IGLV3-12//IGLV1-36//IGLV3-27//IGLV7-46//IGLV4-3//IGLV3-16//IGLV3-19//gene expression for healthy subjects and subjects with SIRS, mildsepsis, severe sepsis.

FIG. 326 shows a box and whiskers plot of NA gene expression for healthysubjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 327 shows a box and whiskers plot of SDHC gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 328 shows a box and whiskers plot of NFXL1 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 329 shows a box and whiskers plot of GLDC gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 330 shows a box and whiskers plot of DCTN5 gene expression forhealthy subjects and subjects with SIRS, mild sepsis, severe sepsis.

FIG. 331 shows a box and whiskers plot of KIAA0101//CSNK1G1 geneexpression for healthy subjects and subjects with SIRS, mild sepsis,severe sepsis.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

An example process for performing biomarker identification will now bedescribed. For the purpose of this example, it is assumed that theprocess is performed at least in part using an electronic processingdevice, such as a processor of a computer system, as will be describedin more detail below.

Furthermore, for the purpose of explanation, different terms will beused to identify biomarkers at different stages of the process. Forexample, the term “reference biomarkers” is used to refer to biomarkerswhose activity has been quantified for a sample population of referenceindividuals having different conditions, stages of different conditions,subtypes of different conditions or with different prognoses. Thedifferent reference biomarkers measured for the individuals may bereferred to as a reference biomarker collection. The term “referencedata” refers to data measured for the individuals in the samplepopulation, and may include quantification of the activity of thebiomarkers measured for each individual, information regarding anyconditions of the individuals, and optionally any other information ofinterest. The number of reference biomarkers will vary, but is typicallymore than 1000 biomarkers.

The term “potential biomarkers” refers to a subset of the referencebiomarkers that have been identified as being potentially useful indistinguishing between different groups of individuals, such asindividuals suffering from different conditions, or having differentstages or prognoses. The number of potential biomarkers will vary, butis typically about 200. The different potential biomarkers may bereferred to as a potential biomarker collection.

The term “remaining reference biomarkers” refers to reference biomarkersremaining in the reference biomarker collection, once potentialbiomarkers have been removed.

The term “signature biomarkers” is used to refer to a subset of thepotential biomarkers that have been identified as being potentiallyuseful in defining signatures that can be used in performing a clinicalassessment, such as to rule in or rule out a specific condition,different stages or severity of conditions, subtypes of differentconditions or different prognoses. The number of signature biomarkerswill vary, but is typically of the order of 10 or less, with thedifferent signature biomarkers identified being referred to as asignature biomarker collection.

It will be appreciated that the above described terms and associateddefinitions are used for the purpose of explanation only and are notintended to be limiting.

In this example, at step 100, the process involves using reference datafrom a plurality of individuals to define a number of groups ofindividuals. The individuals are taken from a reference population,typically including individuals having a range of different conditions,or stages of different conditions, or subtypes of different conditionsor with different prognoses.

The reference data typically includes measurements of a plurality ofreference biomarkers, the measurements including information regardingthe activity, such as the level or abundance, of any expression productor measurable molecule, as will be described in more detail below. Thereference data may also include other additional relevant informationsuch as clinical data regarding one or more conditions suffered by eachindividual. This can include information regarding a presence, absence,degree, stage, severity or progression of a condition, phenotypicinformation, such as details of phenotypic traits, genetic orgenetically regulated information, amino acid or nucleotide relatedgenomics information, results of other tests including imaging,biochemical and hematological assays, other physiological scores such asa SOFA (Sequential Organ Failure Assessment) score, or the like and thisis not intended to be limiting, as will be apparent from the descriptionbelow.

At step 110, a plurality of analysis techniques, such as statisticalanalysis or machine learning techniques, are used to identify a numberof potential biomarkers from the plurality of reference biomarkers thatare potentially useful for distinguishing the groups of individuals,allowing the potential biomarkers to be used in selecting signaturebiomarkers for use in generating signatures for use in clinicalassessments.

The analysis techniques are typically applied in an iterative fashion,with each iteration being used to identify a subset of referencebiomarkers that might prove suitable for use as potential biomarkers. Inone example, as each iteration is performed, the predictive performanceof the reference biomarkers in distinguishing the groups is assessed,with reference biomarkers being identified for use as potentialbiomarkers only in the event that they exceed a predetermined predictiveperformance threshold, such as at least 90%, at least 85% or moretypically, at least 80%. This threshold may be implemented as accuracyin the case of classification or a measure of correlation in the case ofcontinuous outcomes.

Once reference biomarkers are identified for use as potentialbiomarkers, they can be removed from the reference biomarker collection,allowing the next iteration to be performed on the remaining referencebiomarkers. The number of iterations will depend on the analysistechniques and associated parameters used, and can include at least 100,at least 500, at least 1000, at least 2000 and even at least 5000.

The process uses a plurality of different analysis techniques, such asclassification, regression and/or machine learning techniques, allowinga variety of potential biomarkers to be identified. This is performed aseach analysis technique typically operates slightly differently and as aresult will often identify different potential biomarkers, so using theplurality of different analysis techniques ensures that as manypotentially useful biomarkers as possible are captured for use aspotential biomarkers.

The analysis techniques may be performed until the predictiveperformance of the remaining reference biomarkers in the referencebiomarker collection falls below the predetermined threshold and eachtechnique has been used, or may be repeated until a predetermined numberof potential biomarkers, such as at least 100, less than 500 or moretypically about 200, are identified.

Following identification of potential biomarkers, at step 120, a subsetof the potential biomarkers can be optionally identified for use assignature biomarkers, to allow signatures for use in specific clinicalassessments to be determined. This can be achieved in any suitablemanner, but in one example, this involves a further process ofidentifying specific groups relevant to the clinical assessment, andthen performing a further regression or other similar statisticalanalysis to select those potential biomarkers that can be used assignature biomarkers.

Accordingly, in one example, the above described process is used toidentify a subset of measured reference biomarkers that can act aspotential biomarkers, before a more in depth analysis is performed toidentify a subset of potential biomarkers for use as signaturebiomarkers that can be used in specific clinical assessments. As aresult, the above process can act as a coarse filter, allowing arelatively large number of potential biomarkers to be identified thatcan be used in distinguishing the different groups of individuals.

By way of example, many patients suffer from a condition called SystemicInflammatory Response Syndrome (SIRS) (M S Rangel-Frausto, D Pittet, MCostigan, T Hwang, C S Davis, and R P Wenzel, “The Natural History ofthe Systemic Inflammatory Response Syndrome (SIRS). a ProspectiveStudy.,” JAMA: the Journal of the American Medical Association 273, no.2 (Jan. 11, 1995): 117-123.). SIRS is an overwhelming whole bodyreaction that may have an infectious or non-infectious aetiology,whereas sepsis is SIRS that occurs during infection. Both are defined bya number of non-specific host response parameters including changes inheart and respiratory rate, body temperature and white cell counts(Mitchell M Levy et al., “2001 SCCM/ESICM/ACCP/ATS/SIS InternationalSepsis Definitions Conference,” Critical Care Medicine 31, no. 4 (April2003): 1250-1256, doi:10.1097/01.CCM.0000050454.01978.3B.; K Reinhart, MBauer, N C Riedemann, and C S Hartog, “New Approaches to Sepsis:Molecular Diagnostics and Biomarkers,” Clinical Microbiology Reviews 25,no. 4 (Oct. 3, 2012): 609-634, doi:10.1128/CMR.00016-12.) Todifferentiate these conditions they are referred herein to as SIRS (bothconditions), infection-negative SIRS (SIRS without infection, hereafterreferred to as “inSIRS”) and infection-positive SIRS (sepsis, SIRS witha known or suspected infection, hereafter referred to as “ipSIRS”). Thecauses of SIRS are multiple and varied and can include, but are notlimited to, trauma, burns, pancreatitis, endotoxemia, surgery, adversedrug reactions, and infections (local and systemic). Using two patientpopulations of healthy individuals and individuals having SIRS, a coarsefilter can be used to identify which reference biomarkers candistinguish these two groups of individuals, thereby allowing potentialbiomarkers to be identified. A coarse filter could also be used toidentify which reference biomarkers can separate inSIRS patients fromipSIRS patients, both groups of patients having SIRS in common, but eachgroup of patients differing in whether a clinician has determined thepresence of an infection or not.

Following this, more specific and computationally intensive analysescould be performed to identify a subset of potential biomarkers for useas signature biomarkers to answer more specific clinical questions suchas: for patients with ipSIRS which biomarkers can separate out thosewith severe sepsis or septic shock, or provide a prognosis or indicationof likely progression to another stage of disease, or for patients withinSIRS which biomarkers can separate those with pancreatitis from thosefollowing surgery.

Thus, if it is desired to make clinical assessments relating to SIRS,and in particular, inSIRS and ipSIRS, a suite of biomarkers can bequantified for individuals suffering either one of these conditions, aswell as healthy individuals and used as reference biomarkers. These datacan be used to define first groups of individuals having one of the twoconditions or both, as well as of healthy individuals. Potentialbiomarkers can be ascertained that may be used to distinguish thesegroups. For example, the first stage could be used to determinebiomarkers that differentiate healthy individuals and individuals havingSIRS.

Following this, signature biomarkers for specific clinical assessmentswithin these groups, such as distinguishing inSIRS from ipSIRS (rule inand rule out ipSIRS), can be determined. In this case, second groups aredefined that relate to individuals having or not havinginfection-positive or inSIRS, and then signature biomarkers aredetermined from the potential biomarkers.

It can be complex and computationally difficult to select a limitednumber of clinically useful and manageable biomarkers from a large dataset in a single stage. Thus, using a single stage identificationprocess, potentially useful biomarkers can be easily overlooked oromitted, so that the resulting signature biomarkers are not necessarilythe best suited for use in a specific clinical assessment. A particularbenefit of the described approach is that by separating the process intomultiple stages, the chances of overlooking or omitting the discovery ofnew and clinically useful biomarkers is greatly reduced.

The multi-stage approach allows coarse filtering to be used first so asto limit the number of measured reference biomarkers to a moremanageable number of potential biomarkers, so that more specific, andcomputationally intensive, techniques can be used to identify signaturebiomarkers for use in specific clinical assessments. The coarse analysistherefore allows a collection of potential biomarkers to be establishedthat will be relevant to a range of different but related clinicalassessments. A more focussed analysis can then be performed to identifyspecific signature biomarkers, which is less computationally intensivethan attempting to do this for a greater number of biomarkers, and alsohelps ensure the best biomarkers for the clinical assessment areidentified by excluding the noise introduced by many uninformativebiomarkers which have been removed from consideration.

The above approach can therefore allow a large number of measuredreference biomarkers, typically several thousand, to be used as a basisfor the analysis, thereby reducing the likelihood of new and clinicallyrelevant biomarkers being excluded from the resulting potentialbiomarkers, and ultimately signature biomarkers, hence improving theability of the signatures to be clinically useful in assessments.

In one example, the process is performed at least in part using aprocessing system, such as a suitably programmed computer system. Thiscan be performed on a stand-alone computer, with the microprocessorexecuting applications software allowing the above-described method tobe performed. Alternatively, the process can be performed by one or moreprocessing systems operating as part of a distributed architecture, anexample of which will now be described.

In this example, a base station 201 is coupled via a communicationsnetwork, such as the Internet 202, and/or a number of local areanetworks (LANs) 204, to a number of computer systems 203. It will beappreciated that the configuration of the networks 202, 204 are for thepurpose of example only, and in practice the base station 201, computersystems 203 can communicate via any appropriate mechanism, such as viawired or wireless connections, including, but not limited to mobilenetworks, private networks, such as an 802.11 networks, the Internet,LANs, WANs, or the like, as well as via direct or point-to-pointconnections, such as Bluetooth, or the like.

In one example, the base station 201 includes a processing system 210coupled to a database 211. The base station 201 is adapted to be used inanalysing reference data, selecting potential biomarkers, and optionallygenerating signatures for use in clinical assessments. The referencedata may be stored in the database 211 and may be received from thecomputer systems 203, or other remote devices. The base station 201 mayalso be adapted to assist in performing clinical assessments bycomparing individual data relating to a patient or other individual andthen comparing this to the signatures to allow a clinical assessment tobe made. The computer systems 203 are therefore adapted to communicatewith the base station 201, allowing data to be transferred there betweenand/or to control the operation of the base station 201.

Whilst the base station 201 is a shown as a single entity, it will beappreciated that the base station 201 can be distributed over a numberof geographically separate locations, for example by using processingsystems 210 and/or databases 211 that are provided as part of a cloudbased environment.

However, the above-described arrangement is not essential and othersuitable configurations could be used. For example, the process foridentifying biomarkers, as well as any subsequent clinical assessment ofindividual data could be performed on a stand-alone computer system.

An example of a suitable processing system 210 includes at least onemicroprocessor 300, a memory 301, an input/output device 302, such as akeyboard and/or display, and an external interface 303, interconnectedvia a bus 304 as shown. In this example the external interface 303 canbe utilised for connecting the processing system 210 to peripheraldevices, such as the communications networks 202, 204, databases 211,other storage devices, or the like. Although a single external interface303 is shown, this is for the purpose of example only, and in practicemultiple interfaces using various methods (e.g., Ethernet, serial, USB,wireless or the like) may be provided.

In use, the microprocessor 300 executes instructions in the form ofapplications software stored in the memory 301 to allow the biomarkeridentification process to be performed, as well as to perform any otherrequired processes, such as communicating with the computer systems 203.The applications software may include one or more software modules, andmay be executed in a suitable execution environment, such as anoperating system environment, or the like.

Accordingly, it will be appreciated that the processing system 300 maybe formed from any suitable processing system, such as a suitablyprogrammed computer system, PC, web server, network server, or the like.In one particular example, the processing system 100 is a standardprocessing system such as a 32-bit or 64-bit Intel Architecture basedprocessing system, which executes software applications stored onnon-volatile (e.g., hard disk) storage, although this is not essential.However, it will also be understood that the processing system could beany electronic processing device such as a microprocessor, microchipprocessor, logic gate configuration, firmware optionally associated withimplementing logic such as an FPGA (Field Programmable Gate Array), orany other electronic device, system or arrangement.

In one example, the computer system 203 includes at least onemicroprocessor 400, a memory 401, an input/output device 402, such as akeyboard and/or display, and an external interface 403, interconnectedvia a bus 404 as shown. In this example the external interface 403 canbe utilised for connecting the computer system 203 to peripheraldevices, such as the communications networks 202, 204, databases 211,other storage devices, or the like. Although a single external interface403 is shown, this is for the purpose of example only, and in practicemultiple interfaces using various methods (eg. Ethernet, serial, USB,wireless or the like) may be provided.

In use, the microprocessor 400 executes instructions in the form ofapplications software stored in the memory 401 to allow communicationwith the base station 201, for example to allow data to be suppliedthereto and allowing results of any clinical assessment to be displayedto an operator. The computer system 203 may also be used to allow theoperation of the base station 201 to be controlled, for example to allowthe biomarker identification process to be performed remotely.

Accordingly, it will be appreciated that the computer systems 203 may beformed from any suitable processing system, such as a suitablyprogrammed PC, Internet terminal, lap-top, hand-held PC, smart phone,PDA, web server, or the like. Thus, in one example, the processingsystem 100 is a standard processing system such as a 32-bit or 64-bitIntel Architecture based processing system, which executes softwareapplications stored on non-volatile (e.g., hard disk) storage, althoughthis is not essential. However, it will also be understood that thecomputer systems 203 can be any electronic processing device such as amicroprocessor, microchip processor, logic gate configuration, firmwareoptionally associated with implementing logic such as an FPGA (FieldProgrammable Gate Array), or any other electronic device, system orarrangement.

Examples of the biomarker identification process, and subsequent use ina clinical assessment will now be described in further detail. For thepurpose of these examples, it is assumed that reference data, includingthe reference biomarker collection, any potential biomarkers, signaturebiomarkers or signatures can be stored in the database 211, and that thebiomarker identification process is performed using the processingsystem 210 under control of one of the computer systems 203. Thus, it isassumed that the processing system 210 of the base station 201 hostsapplications software for performing the biomarker identificationprocess, with actions performed by the processing system 210 beingperformed by the processor 300 in accordance with instructions stored asapplications software in the memory 301 and/or input commands receivedfrom a user via the I/O device 302, or commands received from thecomputer system 203.

It will also be assumed that the user interacts with applicationsoftware executed by the processing system 210 via a GUI, or the likepresented on the computer system 203. Actions performed by the computersystem 203 are performed by the processor 401 in accordance withinstructions stored as applications software in the memory 402 and/orinput commands received from a user via the I/O device 403. The basestation 201 is typically a server which communicates with the computersystem 203 via a LAN, or the like, depending on the particular networkinfrastructure available.

However, it will be appreciated that the above-described configurationassumed for the purpose of the following examples is not essential, andnumerous other configurations may be used. It will also be appreciatedthat the partitioning of functionality between the computer system 203,and the base station 201 may vary, depending on the particularimplementation.

A second example of a process for determining biomarkers will now bedescribed.

In this example, at step 500 reference data is acquired for a pluralityof individuals with the reference data including at least data regardinga plurality of reference biomarkers, measured for each individual.

The reference data may be acquired in any appropriate manner buttypically this involves obtaining gene expression product data from aplurality of individuals, selected to include individuals diagnosed withone or more conditions of interest, as well as healthy individuals. Theterms “expression” or “gene expression” refer to production of RNAmessage or translation of RNA message into proteins or polypeptides, orboth. Detection of either types of gene expression in use of any of themethods described herein is encompassed by the present invention. Theconditions are typically medical, veterinary or other health statusconditions and may include any illness, disease, stages of disease,disease subtypes, severities of disease, diseases of varying prognosesor the like.

In order to achieve this, gene expression product data are collected,for example by obtaining a biological sample, such as a peripheral bloodsample, and then performing a quantification process, such as a nucleicacid amplification process, including PCR (Polymerase Chain Reaction) orthe like, in order to assess the activity, and in particular, level orabundance of a number of reference biomarkers. Quantified valuesindicative of the relative activity are then stored as part of thereference data.

Example reference biomarkers will be described in more detail below butit will be appreciated that these could include expression products suchas nucleic acid or proteinaceous molecules, as well as other moleculesrelevant in making a clinical assessment. The number of biomarkersmeasured for use as reference biomarkers will vary depending upon thepreferred implementation, but typically include a large number such as,1000, 5000, 10000 or above, although this is not intended to belimiting.

The individuals also typically undergo a clinical assessment allowingany conditions to be clinically identified, and with an indication ofany assessment or condition forming part of the reference data. Whilstany conditions can be assessed, in one example the process is utilizedspecifically to identify conditions such as SIRS, including inSIRS andipSIRS or sepsis. It will be appreciated from the following, however,that this can be applied to a range of different conditions, andreference to SIRS or sepsis is not intended to be limiting.

Additionally, the reference data may include details of one or morephenotypic traits of the individuals and/or their relatives. Phenotypictraits can include information such as the gender, ethnicity, age, orthe like. Additionally, in the case of the technology being applied toindividuals other than humans, this can also include information such asdesignation of a species, breed or the like.

Accordingly, in one example the reference data can include for each ofthe reference individuals an indication of the activity of a pluralityof reference biomarkers, a presence, absence degree, stage, orprogression of a condition, phenotypic information such as phenotypictraits, genetic information and a physiological score such as a SOFAscore.

The reference data is typically collected from individuals presenting ata medical centre with clinical signs relating to relevant any conditionsof interest, and may involve follow-on consultations in order to confirmclinical assessments, as well as to identify changes in biomarkers,and/or clinical signs, and/or severity of clinical signs, over a periodof time. In this latter case, the reference data can include time seriesdata indicative of the progression of a condition, and/or the activityof the reference biomarkers, so that the reference data for anindividual can be used to determine if the condition of the individualis improving, worsening or static. It will also be appreciated that thereference biomarkers are preferably substantially similar for theindividuals within the sample population, so that comparisons ofmeasured activities between individuals can be made.

It will be appreciated that once collected, the reference data can bestored in the database 211 allowing this to be subsequently retrieved bythe processing system 210 for subsequent analysis. The processing system210 also typically stores an indication of an identity of each of thereference biomarkers as a reference biomarker collection.

At step 505, the processing system 210 optionally removes a validationsubgroup of individuals from the reference data prior to determining thepotential biomarkers. This is performed to allow the processing system210 to determine the potential biomarkers using the reference datawithout the validation subgroup so that the validation subgroup can besubsequently used to validate the potential biomarkers or signaturesincluding a number of the potential biomarkers. Thus, data from thevalidation subgroup is used to validate the efficacy of the potential orsignature biomarkers in identifying the presence, absence, degree,stage, severity, prognosis or progression of any one or more of theconditions to ensure the potential or signature biomarkers areeffective, as will be described in more detail below.

In one example, this is achieved by having the processing system 210flag individuals within the validation subgroup or alternatively storethese in either an alternative location within the database 211 or analternative database to the reference data. The validation subgroup ofindividuals is typically selected randomly and may optionally beselected to include individuals having different phenotypic traits. Whena validation subgroup of individuals is removed, the remainingindividuals will simply be referred to as reference data for easethroughout the remaining description.

At step 510, the individuals remaining within the reference data (ieexcluding the validation subgroup) are classified into groups. Thegroups may be defined in any appropriate manner and may be defined basedon any one or more of an indication of a presence, absence, degree,stage, severity, prognosis or progression of a condition, phenotypictraits, other tests or assays, genetic information or measured activityof the reference biomarkers associated with the individuals.

For example, a first selection of groups may be to identify one or moregroups of individuals suffering from SIRS, one or more groups ofindividuals suffering ipSIRS, one or more groups of individualssuffering inSIRS, and one or more groups of healthy individuals. Furthergroups may also be defined for individuals suffering from otherconditions. Additionally, further subdivision may be performed based onphenotypic traits, so groups could be defined based on gender, ethnicityor the like so that a plurality of groups of individuals suffering froma condition are defined, with each group relating to a differentphenotypic trait.

It will also be appreciated, however, that identification of differentgroups can be performed in other manners, for example on the basis ofparticular activities of biomarkers within the biological samples of thereference individuals, and accordingly, reference to conditions is notintended to be limiting and other information may be used as required.

The manner in which classification into groups is performed may varydepending on the preferred implementation. In one example, this can beperformed automatically by the processing system 210, for example, usingunsupervised methods such as Principal Components Analysis (PCA), orsupervised methods such as k-means or Self Organising Map (SOM).Alternatively, this may be performed manually by an operator by allowingthe operator to review reference data presented on a Graphical UserInterface (GUI), and define respective groups using appropriate inputcommands.

Once the groups have been defined, analysis techniques are utilized inorder to identify reference biomarkers that can be utilized topotentially distinguish the groups. The analysis technique typicallyexamines the activity of the reference biomarkers for individuals withinand across the groups, to identify reference biomarkers whose activitiesdiffer between and hence can distinguish groups. A range of differentanalysis techniques can be utilized including, for example, regressionor correlation analysis techniques. Examples of the techniques used caninclude established methods for parametized model building such asPartial Least Squares, Random Forest or Support Vector Machines, usuallycoupled to a feature reduction technique for the selection of thespecific subset of the biomarkers to be used in a signature.

Such techniques are known and described in a number of publications. Forexample, the use of Partial Least Squares is described in “Partial leastsquares: a versatile tool for the analysis of high-dimensional genomicdata” by Boulesteix, Anne-Laure and Strimmer, Korbinian, from Briefingsin Bioinformatics 2007 vol 8. no. 1, pg 32-44. Support Vector machinesare described in “LIBSVM: a library for support vector machines” byChang, C. C. and Lin, C. J. from ACM Transactions on Intelligent Systemsand Technology (TIST), 2011 vol 2, no. 3, pg 27. Standard Random Forestin R language is described in “Classification and Regression by randomForest” by Liaw, A. and Wiener, M., in R news 2002, vol 2, no. 3, pg18-22.

The analysis techniques are implemented by the processing system 210,using applications software, which allows the processing system 210 toperform multiple ones of the analysis techniques in sequence. This isadvantageous as the different analysis techniques typically havedifferent biases and can therefore be used to identify differentpotential biomarkers that can distinguish the groups, thereby reducingthe risk of clinically relevant biomarkers being overlooked.

At step 515 a next analysis technique is selected by the processingsystem 210, with this being implemented at step 520 to identify the bestN reference biomarkers for distinguishing the groups, where the variableN is a predetermined or algorithmically derived number of biomarkerswhose value may vary depending on the analysis technique used and thepreferred implementation, but is typically a relatively small numbercompared to the overall number of biomarkers, such as less than 10, morethan 1, between 2 and 8 and 5. This process typically involves apredictive model to assess the ability of activities of particular onesof the reference biomarkers to distinguish between different groups. Forexample this can examine the manner in which the activity of referencebiomarkers differ between groups, and/or are relatively similar within agroup. This can be performed iteratively for different combinations ofreference biomarkers until a best N of the reference biomarkers areidentified.

At step 525, the processing system 210 determines the predictiveperformance of the identified best N reference biomarkers, when used inthe model, for in distinguishing the relevant groups. The predictiveperformance is typically a parameter determined as part of thecombination of analysis technique and chosen embodying model, as will beappreciated by persons skilled in the art. For example, receiveroperating characteristic (ROC) analysis may be used to determine optimalassay parameters to achieve a specific level of accuracy, specificity,positive predictive value, negative predictive value, and/or falsediscovery rate.

Optionally, a cross-validation approach may be used whereby steps 520and 525 are repeated M times to produce a distribution of M predictiveperformance measures, and N×M selected reference biomarkers. It will beappreciated that there may be none, some, or complete overlap in thesets of selected reference biomarkers for the M iterations. The union(unique set) of selected reference biomarkers from all M iterations isthe set U.

At step 530, the predictive performance is compared to a predeterminedthreshold, which is typically selected dependent upon the preferredimplementation, but may be a relatively low value such as 80%. In thecase of cross-validation, in which steps 520 and 525 are repeated Mtimes, the predictive performance at step 530 is some property of the Mpredictive performance measurements such as the mean, median or maximum.

By example, ruling in ipSIRS might have a lower threshold than rulingout ipSIRS since the clinical risk of treating someone with inSIRS withantibiotics might be considered to be less than not treating someonewith ipSIRS with antibiotics. Thus, it can be appreciated that thethreshold set is influenced by a variety of factors including clinicalutility, patient welfare, disease prevalence, and econometrics of testuse to name a few examples.

At step 535, if it is determined that the predictive performance isabove the threshold, the identified N reference biomarkers are added toa list or collection of potential biomarkers, an indication of which istypically stored in the database 211. In the case of a cross-validationapproach, where the set of unique selected biomarkers (U) may be largerthan the number to be selected as potential biomarkers (N), the N mostfrequently selected biomarkers during the M iterations are identified asthe N reference biomarkers and are then removed from the referencebiomarker collection before further analysis is performed. The processthen returns to step 520 allowing the same analysis technique to beperformed and the next N reference biomarkers identified.

It will therefore be appreciated that this is an iterative techniquethat allows reference biomarkers capable of distinguishing the groups tobe progressively identified with the ability of an additional Nreference biomarkers to act as potential biomarkers being assessed,within each iteration. This process performs a relatively coarsefiltering of reference biomarkers allowing groups of referencebiomarkers with predictive performance above the threshold to beprogressively removed from the reference biomarker collection and addedto the potential biomarker collection.

During this process, if it is determined that the predictive performanceof the N identified reference biomarkers is below the threshold, thenthe process moves to step 540 when it is determined by the processingsystem 210 if all analysis techniques have been used. If not, theprocess returns to step 515 allowing a next analysis technique to beselected.

Thus, it will be appreciated that the iterative process is repeated fora number of different analysis techniques allowing biases between thetechniques to identify different potential biomarkers. Accordingly, thisprocess progressively identifies reference biomarkers useful aspotential biomarkers utilizing a coarse identification process that canbe performed relatively rapidly, and optionally in parallel, over alarge number of reference biomarkers.

At this stage, the potential biomarkers may be utilized in an attempt toclassify the validation subgroup of individuals. In particular, thedifferent activities of the identified potential biomarkers forindividuals within each group are utilized to attempt to classifyindividuals in the validation subgroup into the groups defined at step510. In the event that classification of the validation subgroup issuccessful, potential biomarkers may be retained, whereas if avalidation is unsuccessful potential biomarkers may optionally beremoved from the potential biomarker collection.

In one example, the above-described process is performed over severalthousand different reference biomarkers allowing a collection of severalhundred potential biomarkers to be identified. However, the potentialbiomarkers may not be ideal for answering specific clinical assessmentquestions, such as ruling in a condition, ruling out a condition, ordetermining a stage of progression or likely outcome of a condition ortreatment.

Accordingly, once the potential biomarkers have been identified, morerefined processes are used to allow the processing system 210 toidentify a number of potential biomarkers for use as signaturebiomarkers, in turn allowing signatures to be developed for performingspecific clinical assessments.

In this regard, it will be appreciated that typically clinicians willwant to perform a specific clinical assessment based on a preliminarydiagnosis made using clinical signs, present in a subject presented tothem. Accordingly, a clinician could potentially only need to answer thequestion of whether the subject has ipSIRS, or does not have ipSIRS. Asthe cost, speed and ability to perform a diagnostic test will typicallybe heavily dependent on the number of biomarkers assessed as part of thetest, it is preferable to be able to identify a minimal number ofbiomarkers that are able to answer the specific clinical assessment ofinterest. To address this, the process can use more refined analysis ofthe potential biomarkers to identify those that are most useful inperforming a particular clinical assessment, and hence can be used assignature biomarkers.

Accordingly, at step 545 a next clinical assessment is determined. Thiscan be achieved in any manner, but usually involves having the userdefine the clinical assessment using appropriate input commands. As partof this, at step 550, the processing system 210 is used to identifysecond groups that are relevant to the clinical assessment, for example,by having the user identify criteria, such as the relevant conditionsassociated with each group, or the stage of progression for theindividuals within the groups. This could include, for example, defininggroups of individuals having ipSIRS and those not having ipSIRS, orthose having mild, major, worsening or improving ipSIRS. Whilst it willbe appreciated that the second groups may be the same as the firstgroups previously defined at step 510, more typically the second groupsare more appropriately targeted based on the particular clinicalassessment.

At step 555, the processing system 210 uses a second analysis techniqueto identify a number of the potential biomarkers that best distinguishthe second groups of individuals. In particular, this will attempt toidentify potential biomarkers whose level of activity for theindividuals within the groups, can be used to distinguish the groups.The nature of the analysis technique will vary depending upon thepreferred implementation and can include analysis techniques similar tothose outlined above. Alternatively, different analysis techniques canbe used such as ordinal classification, which differs from regularclassification in that the known order of classes is used withoutassumptions as to their relative similarity to impose extra constraintsin the model leading to more accurate clarification of borderline cases.Such ordinal classification is described in “Support vector ordinalregression” by Chu, W. and Keerthi, S. S., in Neural Computation 2007,vol 19, no. 3, pg 792-815.

An ordinal SVM for classification consists of the same fundamentalelements of any SVM technique that would be familiar to anyone skilledin the art. Namely, the objective is to describe a number of maximallyseparating hyper-planes in the transformed hyperspace defined by thekernel function. An ordinal classifier differs from a regular SVMclassifier in that it imposes an ordinal structure through the use ofthe cost function. This is implemented by adding to cost functions acomponent which penalizes incorrect ranks during execution, as described“Support vector ordinal regression” by Chu et al. (2007, supra).

Typically, the analysis techniques are implemented to identify a limitedoverall number of potential biomarkers that can be used as signaturebiomarkers, and may therefore use more stringent criteria than theanalysis techniques used in steps 515 to 530 above. Alternatively, theanalysis techniques may not be limited in the number of potentialbiomarkers identified, and can instead identify more or less potentialbiomarkers than the predetermined number N, above. Additionally, forthis reason, only a single analysis technique is typically required atthis stage, although this is not essential and multiple second analysistechniques could be used.

At step 560, the processing system 210 determines if the predictiveperformance of the identified potential biomarkers exceeds a secondpredetermined threshold.

Optionally, a cross-validation approach may be used whereby steps 550and 560 are repeated M times to produce a distribution of M predictiveperformance measures, and N×M selected reference biomarkers. It will beappreciated that there may be none, some, or complete overlap in thesets of selected reference biomarkers for the M iterations. The union(unique set) of selected reference biomarkers from all M iterations isthe set U.

Optionally, a consensus approach may be used, whereby steps 555 and 560are repeated multiple times, and the predictive performance measure issome measure of the consensus of the iterations, such as the averagevalue.

At step 565, if it is determined that the predictive performance is notabove the second predetermined threshold, the processing system 210modifies parameters associated with the analysis technique at step 570and the process returns to step 555 allowing the same or alternativepotential biomarkers to be assessed. This process is repeated until asuccessful determination occurs when a limited number of potentialbiomarkers are identified which provide a predictive performance abovethe threshold, in which case the process moves on to step 575.

It will be appreciated that as this is attempting to identify a limitednumber of biomarkers that provide better predictive performance, thesecond predetermined threshold is typically set to be higher that thefirst predetermined threshold used at step 530, and as a result of this,the second analysis technique may be computationally more expensive.Despite this, as the process is only being performed on the basis of thepotential biomarkers and not the entire set of reference biomarkers,this can typically be performed relatively easily.

At step 575, the processing system 210 determines if the identifiedpotential biomarkers are to be excluded. This may occur for any one of anumber of reasons. For example, a limited number of say five biomarkersmay be identified which are capable of providing the required clinicalassessment outcome. However, it may not be possible to use some of thesebiomarkers for legal or technical reasons, in which case the biomarkersmay be excluded. In this case, the excluded biomarkers are removed fromthe potential biomarker database at step 580 and the process returns tostep 555 allowing the analysis to be performed.

It will be appreciated that whilst such excluded biomarkers may beremoved from the reference data at an earlier point in the process, theability to identify excluded biomarkers may be difficult. For example,performing a freedom-to-operate assessment of potential biomarkers canbe an expensive process. It is therefore unfeasible to do this to theentire collection of biomarkers within the reference database or even tothe entire collection of potential biomarkers. Accordingly, thisassessment is only typically made once a potential biomarker has beenidentified at step 555 to 565 as providing a predictive performanceabove the threshold.

In the event that none of the potential biomarkers are excluded, theidentified potential biomarkers are used as signature biomarkers, and anindication of the signature biomarkers is typically stored in asignature biomarker collection in the database 211. The measuredactivities from the reference individuals for the signature biomarkerscan then be used to generate signatures for use in performing theclinical assessment at step 585. The signatures will typically defineactivities or ranges of activities of the signature biomarkers that areindicative of the presence, absence, degree, stage, or progression of acondition. This allows the signatures to be used in performingdiagnostic and/or prognostic assessment of subjects.

For example, an indication of the activity of the signature biomarkerscan be obtained from a sample taken from a test subject, and used toderive a signature indicative of the health status of the test subject.This can then be compared to the signatures derived from the referencedata to assess the likely heath status of the subject.

Following this, at step 590 the process moves on to determine whetherall clinical assessments have been addressed and if not, returns to step545 allowing a next clinical assessment to be selected. Otherwise, theprocess ends at step 595.

Accordingly, it will be appreciated that the above-described methodologyutilizes a staged approach in order to generate potential biomarkers andoptionally, further signature biomarkers, for use in performing clinicalassessments.

The process utilizes an initial coarse filtering based on a plurality ofanalysis techniques in order to identify a limited number of potentialbiomarkers. The limited number of potential biomarkers, which istypically in the region of less than 500, are selected from a largerdatabase of biomarkers as being those most capable of distinguishingbetween different conditions, and/or different stages or progressions ofa condition.

Following this, in a further stage, specific clinical assessments areidentified with additional analysis techniques being used to selectparticular biomarkers from the database of potential biomarkers with theparticular biomarkers being capable of being use in answering thespecific clinical assessments.

A specific example of the above-described process will now be describedwith reference to distinguishing between inSIRS and ipSIRS.

A number of patients clinically identified as having infection negativeSIRS and infection positive SIRS had peripheral blood samples taken(N=141). These samples were run on microarray. The microarray data wasthen normalised and quality control (QC) filtered as per therecommendation of the manufacturer to produce a list of samples with acorresponding clinical diagnosis of SIRS with or without an infection(N=141), and a list of reference biomarkers that passed QC (N=15,989).

The process of building and testing a model will now be described. Inthis example, 10% of the samples are randomly selected to act as thetesting/validation set and are put aside. The remaining 90% of thesamples are the training set, used to identify the potential biomarkers.

A feature selection algorithm coupled to a machine learning model isthen applied to the training set, In this example a Recursive FeatureSelection Support Vector Machine, described for example in “RecursiveSVM feature selection and sample classification for mass-spectrometryand microarray data”, by Xuegong Zhang, Xin Lu, Qian Shi, Xiu-qin Xu,Hon-chiu E Leung, Lyndsay N Harris, James D Iglehart, Alexander Miron,Jun S Liu and Wing H Wong from BMC Bioinformatics 2006, 7:197, was usedto build a model with exactly 10 genes as the input.

Assuming no technical or biological noise and ignoring sample sizeconsiderations, these genes best describe the inherent variabilitybetween inSIRS and ipSIRS samples when using an SVM model, and thereforeprovide the best available separation signature.

For each sample in the testing set, the model is used to predict eitherinSIRS or ipSIRS. If the prediction matches the clinical record for thissample, it is declared a correct prediction. The performance of themodel in this case is measured by accuracy, which can be expressed asthe percentage of correct predictions for the testing set.

Optionally, the building and testing step may be repeated with adifferent random testing and training set. This could be performed anynumber of times depending on the preferred implementation, and in oneexample is performed 100 times. If the accuracy of the model was notsignificantly better than the last 2 iterations (1 way ANOVAp-value>0.95), then the selection of biomarkers was terminated.

If the accuracy remained significantly better than either of the last 2iterations (as described above), then the 10 genes that were selected inthe model (or most frequently appear if repeated runs were used) arethen added to the collection of potentially useful biomarkers, and wereremoved from subsequent iterations.

The biomarker identification process described above and elsewhereherein has been used to identify 319 biomarker genes (hereafter referredto as “inflammatory response syndrome (IRS) biomarker genes”), which aresurrogate markers that are useful for assisting in distinguishing: (1)between SIRS affected subjects (i.e., subject having inSIRS or ipSIRS)and healthy subjects or subjects not affected by SIRS; (2) betweensubjects with inSIRS and subjects with ipSIRS; and/or (3) betweensubjects with different stages of ipSIRS (e.g., sepsis, severe sepsisand septic shock). Based on this identification, the present inventorshave developed various methods and kits, which take advantage of thesebiomarkers to determine the likelihood of the presence or absence of acondition selected from a healthy condition (e.g., a normal condition orone in which inSIRS and inSIRS are absent), SIRS generally (i.e., notdistinguishing between inSIRS or ipSIRS), inSIRS or ipSIRS, or to assessthe likelihood of the presence, absence or risk of development of astage of ipSIRS (e.g., a stage of ipSIRS with a particular severity,illustrative examples of which include mild sepsis, severe sepsis andseptic shock). In advantageous embodiments, the methods and kits involvemonitoring the expression of IRS biomarker genes in blood cells (e.g.,immune cells such as leukocytes), which may be reflected in changingpatterns of RNA levels or protein production that correlate with thepresence of active disease or response to disease.

As used herein, the term SIRS (“systemic inflammatory responsesyndrome”) refers to a clinical response arising from a non-specificinsult with two or more of the following measurable clinicalcharacteristics; a body temperature greater than 38° C. or less than 36°C., a heart rate greater than 90 beats per minute, a respiratory rategreater than 20 per minute, a white blood cell count (total leukocytes)greater than 12,000 per mm³ or less than 4,000 per mm³, or a bandneutrophil percentage greater than 10%. From an immunologicalperspective, it may be seen as representing a systemic response toinsult (e.g., major surgery) or systemic inflammation. As used herein,“inSIRS” includes the clinical response noted above but in the absenceof a systemic infectious process. By contrast, “ipSIRS” includes theclinical response noted above but in the presence of a presumed orconfirmed systemic infectious process. Confirmation of infectiousprocess can be determined using microbiological culture or isolation ofthe infectious agent. From an immunological perspective, ipSIRS may beseen as a systemic response to microorganisms be it local, peripheral ora systemic infection.

The terms “surrogate marker” and “biomarker” are used interchangeablyherein to refer to a parameter whose measurement (e.g., level, presenceor absence) provides information as to the state of a subject. Invarious exemplary embodiments, a plurality of biomarkers is used toassess a condition (e.g., healthy condition, SIRS, inSIRS, ipSIRS, or aparticular stage of ipSIRS). Measurements of the biomarkers may be usedalone or combined with other data obtained regarding a subject in orderto determine the state of the subject biomarker. In some embodiments,the biomarkers are “differentially present” in a sample taken from asubject of one phenotypic status (e.g., having a specified condition) ascompared with another phenotypic status (e.g., not having thecondition). A biomarker may be determined to be “differentially present”in a variety of ways, for example, between different phenotypic statusesif the presence or absence or mean or median level or concentration ofthe biomarker in the different groups is calculated to be statisticallysignificant. Common tests for statistical significance include, amongothers, t-test, ANOVA, Kruskal-Wallis, Wilcoxon, Mann-Whitney and oddsratio.

In some embodiments, the methods and kits involve: (1) correlating areference IRS biomarker profile with the presence or absence of acondition selected from a healthy condition, SIRS, inSIRS, ipSIRS, or aparticular stage of ipSIRS, wherein the reference IRS biomarker profileevaluates at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 etc.) IRSbiomarker; (2) obtaining an IRS biomarker profile of a sample (i.e., “asample IRS biomarker profile”) from a subject, wherein the sample IRSbiomarker profile evaluates for an individual IRS biomarker in thereference IRS biomarker profile a corresponding IRS biomarker; and (3)determining a likelihood of the subject having or not having thecondition based on the sample IRS biomarker profile and the referenceIRS biomarker profile.

As used herein, the term “profile” includes any set of data thatrepresents the distinctive features or characteristics associated with acondition of interest, such as with a particular prediction, diagnosisand/or prognosis of a specified condition as taught herein. The termgenerally encompasses quantification of one or more biomarkers interalia nucleic acid profiles, such as for example gene expression profiles(sets of gene expression data that represents the mRNA levels of one ormore genes associated with a condition of interest), as well as protein,polypeptide or peptide profiles, such as for example protein expressionprofiles (sets of protein expression data that represents the levels ofone or more proteins associated with a condition of interest), and anycombinations thereof.

Biomarker profiles may be created in a number of ways and may be thecombination of measurable biomarkers or aspects of biomarkers usingmethods such as ratios, or other more complex association methods oralgorithms (e.g., rule-based methods), as discussed for example in moredetail below. A biomarker profile comprises at least two measurements,where the measurements can correspond to the same or differentbiomarkers. Thus, for example, distinct reference profiles may representthe prediction of a risk (e.g., an abnormally elevated risk) of having aspecified condition as compared the prediction of no or normal risk ofhaving that condition. In another example, distinct reference profilesmay represent predictions of differing degrees of risk of having aspecified condition.

The terms “subject,” “individual” and “patient” are used interchangeablyherein to refer to any subject, particularly a vertebrate subject, andeven more particularly a mammalian subject. Suitable vertebrate animalsthat fall within the scope of the invention include, but are notrestricted to, any member of the subphylum Chordata including primates,rodents (e.g., mice rats, guinea pigs), lagomorphs (e.g., rabbits,hares), bovines (e.g., cattle), ovines (e.g., sheep), caprines (e.g.,goats), porcines (e.g., pigs), equines (e.g., horses), canines (e.g.,dogs), felines (e.g., cats), avians (e.g., chickens, turkeys, ducks,geese, companion birds such as canaries, budgerigars etc), marinemammals (e.g., dolphins, whales), reptiles (snakes, frogs, lizards,etc.), and fish. A preferred subject is a primate (e.g., a human, ape,monkey, chimpanzee).

IRS biomarkers are suitably expression products of IRS biomarker genes,including polynucleotide and polypeptide expression products. The term“gene” as used herein refers to any and all discrete coding regions ofthe cell's genome, as well as associated non-coding and regulatoryregions. The term “gene” is also intended to mean the open reading frameencoding specific polypeptides, introns, and adjacent 5′ and 3′non-coding nucleotide sequences involved in the regulation ofexpression. In this regard, the gene may further comprise controlsignals such as promoters, enhancers, termination and/or polyadenylationsignals that are naturally associated with a given gene, or heterologouscontrol signals. The DNA sequences may be cDNA or genomic DNA or afragment thereof. The gene may be introduced into an appropriate vectorfor extrachromosomal maintenance or for integration into the host.

As used herein, polynucleotide expression products of IRS biomarkergenes are referred to herein as “IRS biomarker polynucleotides.”Polypeptide expression products of the IRS biomarker genes are referredto herein as “IRS biomarker polypeptides.”

Suitably, individual IRS biomarker genes are selected from the groupconsisting of: TLR5; CD177; VNN1; UBE2J1; IMP3; RNASE2//LOC643332;CLEC4D; C3AR1; GPR56; ARG1; FCGR1A//FCGR1B//FCGR1C; C11orf82; FAR2;GNLY; GALNT3; OMG; SLC37A3; BMX//HNRPDL; STOM; TDRD9; KREMEN1; FAIM3;CLEC4E; IL18R1; ACER3; ERLIN1; TGFBR1; FKBP5//LOC285847; GPR84; C7orf53;PLB1; DSE; PTGDR; CAMK4; DNAJC13; TNFAIP6;FOXD4L3//FOXD4L6//FOXD4//FOXD4L1//FOXD4L2//FOXD4L4//FOXD4L5;MMP9//LOC100128028; GSR; KLRF1; SH2D1B; ANKRD34B; SGMS2; B3GNT5//MCF2L2;GK3P//GK; PFKFB2; PICALM; METTL7B; HIST1H4C; C9orf72; HIST1H3I; SLC15A2;TLR10; ADM; CD274; CRIP1; LRRN3; HLA-DPB1; VAMP2; SMPDL3A; IFI16; JKAMP;MRPL41; SLC1A3; OLFM4; CASS4; TCN1; WSB2; CLU; ODZ1; KPNA5; PLACE; CD63;HPSE; C1orf161; DDAH2; KLRK1//KLRC4; ATP13A3; ITK; PMAIP1; LOC284757;GOT2; PDGFC; B3GAT3; HIST1H4E; HPGD; FGFBP2; LRRC70//IPO11;TMEM144//LOC285505; CDS2; BPI; ECHDC3; CCR3; HSPC159; OLAH;PPP2R5A//SNORA16B; TMTC1; EAF2//HCG11//LOC647979; RCBTB2//LOC100131993;SEC24A//SAR1B; SH3PXD2B; HMGB2; KLRD1; CHI3L1; FRMD3; SLC39A9; GIMAP7;ANAPC11; EXOSC4; gene for IL-1beta-regulated neutrophil survival proteinas set forth in GenBank Accession No. AF234262; INSIG1; FOLR3//FOLR2;RUNX2; PRR13//PCBP2; HIST1H4L; LGALS1; CCR1; TPST1; HLA-DRA; CD163;FFAR2; PHOSPHO1; PPIF; MTHFS; DNAJC9//FAM149B1//RPL26; LCN2; EIF2AK2;LGALS2; SIAE; AP3B2; ABCA13; gene for transcript set forth in GenBankAccession No. AK098012; EFCAB2; HIST1H2AA; HINT1; HIST1H3J; CDA; SAP30;AGTRAP; SUCNR1; MTRR; PLA2G7; AIG1; PCOLCE2; GAB2; HS2ST1//UBA2;HIST1H3A; C22orf37; HLA-DPA1; VOPP1//LOC100128019; SLC39A8; MKI67;SLC11A1; AREG; ABCA1; DAAM2//LOC100131657; LTF; TREML1; GSTO1; PTGER2;CEACAM8; CLEC4A; PMS2CL//PMS2; RETN; PDE3B; SULF2; NEK6//LOC100129034;CENPK; TRAF3; GPR65; IRF4; MACF1; AMFR; RPL17//SNORD58B; IRS2; JUP;CD24; GALNT2; HSP90AB1//HSP90AB3P//HSP90AB2P; GLT25D1; OR9A2; HDHD1A;ACTA2; ACPL2; LRRFIP1; KCNMA1; OCR1; ITGA4//CERKL;EIF1AX//SCARNA9L//EIF1AP1; SFRS9; DPH3; ERGIC1; CD300A; NF-E4; MINPP1;TRIM21; ZNF28; NPCDR1; gene for protein FL321394 as set forth in GenBankAccession No. BC013935; gene for transcript set forth in GenBankAccession No. AK000992; ICAM1; TAF13; P4HA1//RPL17; C15orf54; KLHL5;HAL; DLEU2//DLEU2L; ANKRD28; LY6G5B//CSNK2B;KIAA1257//ACAD9//LOC100132731; MGST3; KIAA0746; HSPB1//HSPBL2; CCR4;TYMS; RRP12//LOC644215; CCDC125; HIST1H2BM; PDK4; ABCG1; IL1B; THBS1;ITGA2B; LHFP; LAIR1//LAIR2; HIST1H3B; ZRANB1; TIMM10; FSD1L//GARNL1;HIST1H2AJ//HIST1H2AI; PTGS1; gene for transcript set forth in GenBankAccession No. BC008667; UBE2F//C20orf194//SCLY; HIST1H3C; FAM118A;CCRL2; E2F6; MPZL3; SRXN1; CD151; HIST1H3H; FSD1L; RFESD//SPATA9; TPX2;S100B; ZNF587//ZNF417; PYHIN1; KIAA1324; CEACAM6//CEACAM5; APOLD1;FABP2; KDM6B//TMEM88;IGK@//IGKC//IGKV1-5//IGKV3D-11//IGKV3-20//IGKV3D-15//LOC440871//LOC652493//LOC100291464//LOC652694//IGKV3-15//LOC650405//LOC100291682;MYL9; HIST1H2BJ; TAAR1; CLC; CYP4F3//CYP4F2; CEP97; SON; IRF1; SYNE2;MME; LASS4; DEFA4//DEFA8P; C7orf58; DYNLL1; gene for transcript setforth in GenBank Accession No. AY461701; MPO; CPM; TSHZ2; PLIN2;FAM118B; B4GALT3; RASA4HRASA4PHRASA4B//POLR2J4//LOC100132214;CTSL1//CTSLL3; NP; ATF7; SPARC; PLB1; C4orf3; POLE2; TNFRSF17; FBXL13;PLEKHA3; TMEM62//SPCS2//LOC653566; RBP7; PLEKHF2; RGS2;ATP6V0D1//LOC100132855; RPIA; CAMK1D; IL1RL1; CMTM5; AIF1; CFD; MPZL2;LOC100128751; IGJ; CDC26; PPP1R2//PPP1R2P3; IL5RA; ARL17P1//ARL17;ATP5L//ATP5L2; TAS2R31; HIST2H2BF//HIST2H3D; CALM2//C2orf61; SPATA6;IGLV6-57; C1orf128; KRTAP15-1; IFI44;IGL@//IGLV1-44//LOC96610//IGLV2-23//IGLC1//IGLV2-18//IGLV5-45//IGLV3-25//IGLV3-12//IGLV1-36//IGLV3-27//IGLV7-46//IGLV4-3//IGLV3-16//IGLV3-19//IGLV7-43//IGLV3-22//IGLV5-37//IGLV10-54//IGLV8-61//LOC651536;gene for transcript set forth in GenBank Accession No. BCO34024; SDHC;NFXL1; GLDC; DCTN5; and KIAA0101//CSNK1G1.

As used herein, the term “likelihood” is used as a measure of whethersubjects with a particular IRS biomarker profile actually have acondition (or not) based on a given mathematical model. An increasedlikelihood for example may be relative or absolute and may be expressedqualitatively or quantitatively. For instance, an increased risk may beexpressed as simply determining the subject's level of a given IRSbiomarker and placing the test subject in an “increased risk” category,based upon previous population studies. Alternatively, a numericalexpression of the test subject's increased risk may be determined basedupon IRS biomarker level analysis.

As used herein, the term “probability” refers strictly to theprobability of class membership for a sample as determined by a givenmathematical model and is construed to be equivalent likelihood in thiscontext.

In some embodiments, likelihood is assessed by comparing the level orabundance of individual IRS biomarkers to one or more preselected orthreshold levels. Thresholds may be selected that provide an acceptableability to predict diagnosis, prognostic risk, treatment success, etc.In illustrative examples, receiver operating characteristic (ROC) curvesare calculated by plotting the value of a variable versus its relativefrequency in two populations in which a first population has a firstcondition or risk and a second population has a second condition or risk(called arbitrarily, for example, “healthy condition” and “SIRS,”“healthy condition” and “inSIRS,” “healthy condition” and “ipSIRS,”“inSIRS” and “ipSIRS,” “mild sepsis” and “severe sepsis,” “severesepsis” and “septic shock,” “mild sepsis” and “septic shock,” or “lowrisk” and “high risk”).

For any particular IRS biomarker, a distribution of IRS biomarker levelsfor subjects with and without a disease will likely overlap. Under suchconditions, a test does not absolutely distinguish a first condition anda second condition with 100% accuracy, and the area of overlap indicateswhere the test cannot distinguish the first condition and the secondcondition. A threshold is selected, above which (or below which,depending on how an IRS biomarker changes with a specified condition orprognosis) the test is considered to be “positive” and below which thetest is considered to be “negative.” The area under the ROC curve (AUC)provides the C-statistic, which is a measure of the probability that theperceived measurement will allow correct identification of a condition(see, e.g., Hanley et al., Radiology 143: 29-36 (1982).

Alternatively, or in addition, thresholds may be established byobtaining an earlier biomarker result from the same patient, to whichlater results may be compared. In these embodiments, the individual ineffect acts as their own “control group.” In biomarkers that increasewith condition severity or prognostic risk, an increase over time in thesame patient can indicate a worsening of the condition or a failure of atreatment regimen, while a decrease over time can indicate remission ofthe condition or success of a treatment regimen.

In some embodiments, a positive likelihood ratio, negative likelihoodratio, odds ratio, and/or AUC or receiver operating characteristic (ROC)values are used as a measure of a method's ability to predict risk or todiagnose a disease or condition. As used herein, the term “likelihoodratio” is the probability that a given test result would be observed ina subject with a condition of interest divided by the probability thatthat same result would be observed in a patient without the condition ofinterest. Thus, a positive likelihood ratio is the probability of apositive result observed in subjects with the specified conditiondivided by the probability of a positive results in subjects without thespecified condition. A negative likelihood ratio is the probability of anegative result in subjects without the specified condition divided bythe probability of a negative result in subjects with specifiedcondition. The term “odds ratio,” as used herein, refers to the ratio ofthe odds of an event occurring in one group (e.g., a healthy conditiongroup) to the odds of it occurring in another group (e.g., a SIRS group,an inSIRS group, an ipSIRS group, or a group with particular stage ofipSIRS), or to a data-based estimate of that ratio. The term “area underthe curve” or “AUC” refers to the area under the curve of a receiveroperating characteristic (ROC) curve, both of which are well known inthe art. AUC measures are useful for comparing the accuracy of aclassifier across the complete data range. Classifiers with a greaterAUC have a greater capacity to classify unknowns correctly between twogroups of interest (e.g., a healthy condition IRS biomarker profile anda SIRS, inSIRS, ipSIRS, or ipSIRS stage IRS biomarker profile). ROCcurves are useful for plotting the performance of a particular feature(e.g., any of the IRS biomarkers described herein and/or any item ofadditional biomedical information) in distinguishing or discriminatingbetween two populations (e.g., cases having a condition and controlswithout the condition). Typically, the feature data across the entirepopulation (e.g., the cases and controls) are sorted in ascending orderbased on the value of a single feature. Then, for each value for thatfeature, the true positive and false positive rates for the data arecalculated. The sensitivity is determined by counting the number ofcases above the value for that feature and then dividing by the totalnumber of cases. The specificity is determined by counting the number ofcontrols below the value for that feature and then dividing by the totalnumber of controls. Although this definition refers to scenarios inwhich a feature is elevated in cases compared to controls, thisdefinition also applies to scenarios in which a feature is lower incases compared to the controls (in such a scenario, samples below thevalue for that feature would be counted). ROC curves can be generatedfor a single feature as well as for other single outputs, for example, acombination of two or more features can be mathematically combined(e.g., added, subtracted, multiplied, etc.) to produce a single value,and this single value can be plotted in a ROC curve. Additionally, anycombination of multiple features, in which the combination derives asingle output value, can be plotted in a ROC curve. These combinationsof features may comprise a test. The ROC curve is the plot of thesensitivity of a test against the specificity of the test, wheresensitivity is traditionally presented on the vertical axis andspecificity is traditionally presented on the horizontal axis. Thus,“AUC ROC values” are equal to the probability that a classifier willrank a randomly chosen positive instance higher than a randomly chosennegative one. An AUC ROC value may be thought of as equivalent to theMann-Whitney U test, which tests for the median difference betweenscores obtained in the two groups considered if the groups are ofcontinuous data, or to the Wilcoxon test of ranks.

In some embodiments, at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,or more) IRS biomarker or a panel if IRS biomarkers is selected todiscriminate between subjects with a first condition and subjects with asecond condition with at least about 50%, 55% 60%, 65%, 70%, 75%, 80%,85%, 90%, 95% accuracy or having a C-statistic of at least about 0.50,0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95.

In the case of a positive likelihood ratio, a value of 1 indicates thata positive result is equally likely among subjects in both the“condition” and “control” groups; a value greater than 1 indicates thata positive result is more likely in the condition group; and a valueless than 1 indicates that a positive result is more likely in thecontrol group. In this context, “condition” is meant to refer to a grouphaving one characteristic (e.g., the presence of a healthy condition,SIRS, inSIRS, ipSIRS, or a particular stage of ipSIRS) and “control”group lacking the same characteristic. In the case of a negativelikelihood ratio, a value of 1 indicates that a negative result isequally likely among subjects in both the “condition” and “control”groups; a value greater than 1 indicates that a negative result is morelikely in the “condition” group; and a value less than 1 indicates thata negative result is more likely in the “control” group. In the case ofan odds ratio, a value of 1 indicates that a positive result is equallylikely among subjects in both the condition” and “control” groups; avalue greater than 1 indicates that a positive result is more likely inthe “condition” group; and a value less than 1 indicates that a positiveresult is more likely in the “control” group. In the case of an AUC ROCvalue, this is computed by numerical integration of the ROC curve. Therange of this value can be 0.5 to 1.0. A value of 0.5 indicates that aclassifier (e.g., a IRS biomarker profile) is no better than a 50%chance to classify unknowns correctly between two groups of interest,while 1.0 indicates the relatively best diagnostic accuracy. In certainembodiments, IRS biomarkers and/or IRS biomarker panels are selected toexhibit a positive or negative likelihood ratio of at least about 1.5 ormore or about 0.67 or less, at least about 2 or more or about 0.5 orless, at least about 5 or more or about 0.2 or less, at least about 10or more or about 0.1 or less, or at least about 20 or more or about 0.05or less.

In certain embodiments, IRS biomarkers and/or IRS biomarker panels areselected to exhibit an odds ratio of at least about 2 or more or about0.5 or less, at least about 3 or more or about 0.33 or less, at leastabout 4 or more or about 0.25 or less, at least about 5 or more or about0.2 or less, or at least about 10 or more or about 0.1 or less.

In certain embodiments, IRS biomarkers and/or IRS biomarker panels areselected to exhibit an AUC ROC value of greater than 0.5, preferably atleast 0.6, more preferably 0.7, still more preferably at least 0.8, evenmore preferably at least 0.9, and most preferably at least 0.95.

In some cases, multiple thresholds may be determined in so-called“tertile,” “quartile,” or “quintile” analyses. In these methods, the“diseased” and “control groups” (or “high risk” and “low risk”) groupsare considered together as a single population, and are divided into 3,4, or 5 (or more) “bins” having equal numbers of individuals. Theboundary between two of these “bins” may be considered “thresholds.” Arisk (of a particular diagnosis or prognosis for example) can beassigned based on which “bin” a test subject falls into.

In other embodiments, particular thresholds for the IRS biomarker(s)measured are not relied upon to determine if the biomarker level(s)obtained from a subject are correlated to a particular diagnosis orprognosis. For example, a temporal change in the biomarker(s) can beused to rule in or out one or more particular diagnoses and/orprognoses. Alternatively, IRS biomarker(s) are correlated to acondition, disease, prognosis, etc., by the presence or absence of oneor more IRS biomarkers in a particular assay format. In the case of IRSbiomarker panels, the present invention may utilize an evaluation of theentire profile of IRS biomarkers to provide a single result value (e.g.,a “panel response” value expressed either as a numeric score or as apercentage risk). In such embodiments, an increase, decrease, or otherchange (e.g., slope over time) in a certain subset of IRS biomarkers maybe sufficient to indicate a particular condition or future outcome inone patient, while an increase, decrease, or other change in a differentsubset of IRS biomarkers may be sufficient to indicate the same or adifferent condition or outcome in another patient.

In certain embodiments, a panel of IRS biomarkers is selected to assistin distinguishing a pair of groups (i.e., assist in assessing whether asubject has an increased likelihood of being in one group or the othergroup of the pair) selected from “healthy condition” and “SIRS,”“healthy condition” and “inSIRS,” “healthy condition” and “ipSIRS,”“inSIRS” and “ipSIRS,” “mild sepsis” and “severe sepsis,” “severesepsis” and “septic shock,” “mild sepsis” and “septic shock,” or “lowrisk” and “high risk” with at least about 70%, 80%, 85%, 90% or 95%sensitivity, suitably in combination with at least about 70% 80%, 85%,90% or 95% specificity. In some embodiments, both the sensitivity andspecificity are at least about 75%, 80%, 85%, 90% or 95%.

The phrases “assessing the likelihood” and “determining the likelihood,”as used herein, refer to methods by which the skilled artisan canpredict the presence or absence of a condition (e.g., a conditionselected from healthy condition, SIRS, inSIRS, ipSIRS, or a particularstage of ipSIRS) in a patient. The skilled artisan will understand thatthis phrase includes within its scope an increased probability that acondition is present or absence in a patient; that is, that a conditionis more likely to be present or absent in a subject. For example, theprobability that an individual identified as having a specifiedcondition actually has the condition may be expressed as a “positivepredictive value” or “PPV.” Positive predictive value can be calculatedas the number of true positives divided by the sum of the true positivesand false positives. PPV is determined by the characteristics of thepredictive methods of the present invention as well as the prevalence ofthe condition in the population analysed. The statistical algorithms canbe selected such that the positive predictive value in a populationhaving a condition prevalence is in the range of 70% to 99% and can be,for example, at least 70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%,84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%,98%, or 99%.

In other examples, the probability that an individual identified as nothaving a specified condition actually does not have that condition maybe expressed as a “negative predictive value” or “NPV.” Negativepredictive value can be calculated as the number of true negativesdivided by the sum of the true negatives and false negatives. Negativepredictive value is determined by the characteristics of the diagnosticor prognostic method, system, or code as well as the prevalence of thedisease in the population analysed. The statistical methods and modelscan be selected such that the negative predictive value in a populationhaving a condition prevalence is in the range of about 70% to about 99%and can be, for example, at least about 70%, 75%, 76%, 77%, 78%, 79%,80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%,94%, 95%, 96%, 97%, 98%, or 99%.

In some embodiments, a subject is determined as having a significantlikelihood of having or not having a specified condition. By“significant likelihood” is meant that the subject has a reasonableprobability (0.6, 0.7, 0.8, 0.9 or more) of having, or not having, aspecified condition.

The IRS biomarker analysis of the present invention permits thegeneration of high-density data sets that can be evaluated usinginformatics approaches. High data density informatics analytical methodsare known and software is available to those in the art, e.g., clusteranalysis (Pirouette, Informetrix), class prediction (SIMCA-P, Umetrics),principal components analysis of a computationally modeled dataset(SIMCA-P, Umetrics), 2D cluster analysis (GeneLinker Platinum, ImprovedOutcomes Software), and metabolic pathway analysis(biotech.icmb.utexas.edu). The choice of software packages offersspecific tools for questions of interest (Kennedy et al., Solving DataMining Problems Through Pattern Recognition. Indianapolis: Prentice HallPTR, 1997; Golub et al., (2999) Science 286:531-7; Eriksson et al.,Multi and Megavariate Analysis Principles and Applications: Umetrics,Umea, 2001). In general, any suitable mathematic analyses can be used toevaluate at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, et.) IRSbiomarker in an IRS biomarker profile with respect to a conditionselected from healthy condition, SIRS, inSIRS, ipSIRS, or a particularstage of ipSIRS. For example, methods such as multivariate analysis ofvariance, multivariate regression, and/or multiple regression can beused to determine relationships between dependent variables (e.g.,clinical measures) and independent variables (e.g., levels of IRSbiomarkers). Clustering, including both hierarchical andnon-hierarchical methods, as well as non-metric Dimensional Scaling canbe used to determine associations or relationships among variables andamong changes in those variables.

In addition, principal component analysis is a common way of reducingthe dimension of studies, and can be used to interpret thevariance-covariance structure of a data set. Principal components may beused in such applications as multiple regression and cluster analysis.Factor analysis is used to describe the covariance by constructing“hidden” variables from the observed variables. Factor analysis may beconsidered an extension of principal component analysis, where principalcomponent analysis is used as parameter estimation along with themaximum likelihood method. Furthermore, simple hypothesis such asequality of two vectors of means can be tested using Hotelling's Tsquared statistic.

In some embodiments, the data sets corresponding to IRS biomarkerprofiles are used to create a diagnostic or predictive rule or modelbased on the application of a statistical and machine learningalgorithm. Such an algorithm uses relationships between an IRS biomarkerprofile and a condition selected from healthy condition, SIRS, inSIRS,ipSIRS, or a particular stage of ipSIRS observed in control subjects ortypically cohorts of control subjects (sometimes referred to as trainingdata), which provides combined control or reference IRS biomarkerprofiles for comparison with IRS biomarker profiles of a subject. Thedata are used to infer relationships that are then used to predict thestatus of a subject, including the presence or absence of one of theconditions referred to above.

Practitioners skilled in the art of data analysis recognize that manydifferent forms of inferring relationships in the training data may beused without materially changing the present invention. The datapresented in the Tables and Examples herein has been used to generateillustrative minimal combinations of IRS biomarkers (models) thatdifferentiate between two conditions selected from healthy condition,SIRS, inSIRS, ipSIRS, or a particular stage of ipSIRS using featureselection based on AUC maximisation in combination with support vectormachine classification. Tables 1-15 provide illustrative lists of IRSbiomarkers ranked according to their p value and FIGS. 1-331 illustratethe ability of each IRS biomarker to distinguish between at least two ofthe conditions. Illustrative models comprising at least about 2 IRSbiomarkers were able to discriminate between two control groups asdefined above with significantly improved positive predictive valuescompared to conventional methodologies.

The term “correlating” generally refers to determining a relationshipbetween one type of data with another or with a state. In variousembodiments, correlating an IRS biomarker profile with the presence orabsence of a condition (e.g., a condition selected from a healthycondition, SIRS, inSIRS, ipSIRS, or a particular stage of ipSIRS)comprises determining the presence, absence or amount of at least oneIRS biomarker in a subject that suffers from that condition; or inpersons known to be free of that condition. In specific embodiments, aprofile of IRS biomarker levels, absences or presences is correlated toa global probability or a particular outcome, using receiver operatingcharacteristic (ROC) curves.

Thus, in some embodiments, evaluation of IRS biomarkers includesdetermining the levels of individual IRS biomarkers, which correlatewith the presence or absence of a condition, as defined above. Incertain embodiments, the techniques used for detection of IRS biomarkerswill include internal or external standards to permit quantitative orsemi-quantitative determination of those biomarkers, to thereby enable avalid comparison of the level of the IRS biomarkers in a biologicalsample with the corresponding IRS biomarkers in a reference sample orsamples. Such standards can be determined by the skilled practitionerusing standard protocols. In specific examples, absolute values for thelevel or functional activity of individual expression products aredetermined.

In semi-quantitative methods, a threshold or cut-off value is suitablydetermined, and is optionally a predetermined value. In particularembodiments, the threshold value is predetermined in the sense that itis fixed, for example, based on previous experience with the assayand/or a population of affected and/or unaffected subjects.Alternatively, the predetermined value can also indicate that the methodof arriving at the threshold is predetermined or fixed even if theparticular value varies among assays or may even be determined for everyassay run.

In some embodiments, the level of an IRS biomarker is normalized againsta housekeeping biomarker. The term “housekeeping biomarker” refers to abiomarker or group of biomarkers (e.g., polynucleotides and/orpolypeptides), which are typically found at a constant level in the celltype(s) being analysed and across the conditions being assessed. In someembodiments, the housekeeping biomarker is a “housekeeping gene.” A“housekeeping gene” refers herein to a gene or group of genes whichencode proteins whose activities are essential for the maintenance ofcell function and which are typically found at a constant level in thecell type(s) being analysed and across the conditions being assessed.

Generally, the levels of individual IRS biomarkers in an IRS biomarkerprofile are derived from a biological sample. The term “biologicalsample” as used herein refers to a sample that may be extracted,untreated, treated, diluted or concentrated from an animal. Thebiological sample is suitably a biological fluid such as whole blood,serum, plasma, saliva, urine, sweat, ascitic fluid, peritoneal fluid,synovial fluid, amniotic fluid, cerebrospinal fluid, tissue biopsy, andthe like. In certain embodiments, the biological sample contains blood,especially peripheral blood, or a fraction or extract thereof.Typically, the biological sample comprises blood cells such as mature,immature or developing leukocytes, including lymphocytes,polymorphonuclear leukocytes, neutrophils, monocytes, reticulocytes,basophils, coelomocytes, hemocytes, eosinophils, megakaryocytes,macrophages, dendritic cells natural killer cells, or fraction of suchcells (e.g., a nucleic acid or protein fraction). In specificembodiments, the biological sample comprises leukocytes includingperipheral blood mononuclear cells (PBMC).

The term “nucleic acid” or “polynucleotide” refers to a polymer,typically a heteropolymer, of nucleotides or the sequence of thesenucleotides from the 5′ to 3′ end of a nucleic acid molecule andincludes DNA or RNA molecules, illustrative examples of which includeRNA, mRNA, siRNA, miRNA, hpRNA, cRNA, cDNA or DNA. The term encompassesa polymeric form of nucleotides that is linear or branched, single ordouble stranded, or a hybrid thereof. The term also encompasses RNA/DNAhybrids. Nucleic acid sequences provided herein are presented herein inthe 5′ to 3′ direction, from left to right and are represented using thestandard code for representing the nucleotide characters as set forth inthe U.S. sequence rules, 37 CFR 1.821-1.825 and the World IntellectualProperty Organization (WIPO) Standard ST.25.

“Protein,” “polypeptide” and “peptide” are used interchangeably hereinto refer to a polymer of amino acid residues and to variants andsynthetic analogues of the same.

Suitably, the levels of individual IRS biomarkers in a reference IRSbiomarker profile are derived from IRS biomarker samples obtained fromone or more control subjects having that condition (e.g., “healthycontrol subjects,” “SIRS control subjects,” “inSIRS control subjects,”“ipSIRS control subjects,” “control subjects with a particular stage ofipSIRS,” illustrative examples of which include “mild sepsis controlsubjects,” “severe sepsis control subjects,” and “septic shock controlsubjects,” etc.), which are also referred to herein as control groups(e.g., “healthy control group,” “SIRS control group,” “inSIRS controlgroup,” “ipSIRS control group,” “ipSIRS stage group,” illustrativeexamples of which include “mild sepsis control group,” “severe sepsiscontrol group,” and “septic shock control group,” etc.). By “obtained”is meant to come into possession. Biological or reference samples soobtained include, for example, nucleic acid extracts or polypeptideextracts isolated or derived from a particular source. For instance, theextract may be isolated directly from a biological fluid or tissue of asubject.

As used herein the terms “level” and “amount” are used interchangeablyherein to refer to a quantitative amount (e.g., weight or moles), asemi-quantitative amount, a relative amount (e.g., weight % or mole %within class or a ratio), a concentration, and the like. Thus, theseterms encompasses absolute or relative amounts or concentrations of IRSbiomarkers in a sample, including ratios of levels of IRS biomarkers,and odds ratios of levels or ratios of odds ratios. IRS biomarker levelsin cohorts of subjects may be represented as mean levels and standarddeviations as shown in the Tables and Figures herein.

In some embodiments, the level of at least one (e.g., 1, 2, 3, 4, 5, 6,7, 8, 9, 10 etc.) IRS biomarker of the subject's sample IRS biomarkerprofile is compared to the level of a corresponding IRS biomarker in thereference IRS biomarker profile. By “corresponding IRS biomarker” ismeant an IRS biomarker that is structurally and/or functionally similarto a reference IRS biomarker. Representative corresponding IRSbiomarkers include expression products of allelic variants (same locus),homologs (different locus), and orthologs (different organism) ofreference IRS biomarker genes. Nucleic acid variants of reference IRSbiomarker genes and encoded IRS biomarker polynucleotide expressionproducts can contain nucleotide substitutions, deletions, inversionsand/or insertions. Variation can occur in either or both the coding andnon-coding regions. The variations can produce both conservative andnon-conservative amino acid substitutions (as compared in the encodedproduct). For nucleotide sequences, conservative variants include thosesequences that, because of the degeneracy of the genetic code, encodethe amino acid sequence of a reference IRS polypeptide.

Generally, variants of a particular IRS biomarker gene or polynucleotidewill have at least about 40%, 45%, 50%, 51%, 52%, 53%, 54%, 55%, 56%,57%, 58%, 59% 60%, 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69% 70%, 71%,72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%,86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% ormore sequence identity to that particular nucleotide sequence asdetermined by sequence alignment programs known in the art using defaultparameters. In some embodiments, the IRS biomarker gene orpolynucleotide displays at least about 40%, 45%, 50%, 51%, 52%, 53%,54%, 55%, 56%, 57%, 58%, 59% 60%, 61%, 62%, 63%, 64%, 65%, 66%, 67%,68%, 69% 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%,82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%,96%, 97%, 98%, 99% or more sequence identity to a nucleotide sequenceselected from any one of SEQ ID NO: 1-319.

Corresponding IRS biomarkers also include amino acid sequence thatdisplays substantial sequence similarity or identity to the amino acidsequence of a reference IRS biomarker polypeptide. In general, an aminoacid sequence that corresponds to a reference amino acid sequence willdisplay at least about 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79,80, 81, 82, 83, 84, 85, 86, 97, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97,98, 99% or even up to 100% sequence similarity or identity to areference amino acid sequence selected from any one of SEQ ID NO:320-619.

In some embodiments, calculations of sequence similarity or sequenceidentity between sequences are performed as follows:

To determine the percent identity of two amino acid sequences, or of twonucleic acid sequences, the sequences are aligned for optimal comparisonpurposes (e.g., gaps can be introduced in one or both of a first and asecond amino acid or nucleic acid sequence for optimal alignment andnon-homologous sequences can be disregarded for comparison purposes). Insome embodiments, the length of a reference sequence aligned forcomparison purposes is at least 30%, usually at least 40%, more usuallyat least 50%, 60%, and even more usually at least 70%, 80%, 90%, 100% ofthe length of the reference sequence. The amino acid residues ornucleotides at corresponding amino acid positions or nucleotidepositions are then compared. When a position in the first sequence isoccupied by the same amino acid residue or nucleotide at thecorresponding position in the second sequence, then the molecules areidentical at that position. For amino acid sequence comparison, when aposition in the first sequence is occupied by the same or similar aminoacid residue (i.e., conservative substitution) at the correspondingposition in the second sequence, then the molecules are similar at thatposition.

The percent identity between the two sequences is a function of thenumber of identical amino acid residues shared by the sequences atindividual positions, taking into account the number of gaps, and thelength of each gap, which need to be introduced for optimal alignment ofthe two sequences. By contrast, the percent similarity between the twosequences is a function of the number of identical and similar aminoacid residues shared by the sequences at individual positions, takinginto account the number of gaps, and the length of each gap, which needto be introduced for optimal alignment of the two sequences.

The comparison of sequences and determination of percent identity orpercent similarity between sequences can be accomplished using amathematical algorithm. In certain embodiments, the percent identity orsimilarity between amino acid sequences is determined using theNeedleman and Wunsch, (1970, J. Mol. Biol. 48: 444-453) algorithm whichhas been incorporated into the GAP program in the GCG software package(available at http://www.gcg.com), using either a Blossum 62 matrix or aPAM250 matrix, and a gap weight of 16, 14, 12, 10, 8, 6, or 4 and alength weight of 1, 2, 3, 4, 5, or 6. In specific embodiments, thepercent identity between nucleotide sequences is determined using theGAP program in the GCG software package (available athttp://www.gcg.com), using a NWSgapdna.CMP matrix and a gap weight of40, 50, 60, 70, or 80 and a length weight of 1, 2, 3, 4, 5, or 6. Annon-limiting set of parameters (and the one that should be used unlessotherwise specified) includes a Blossum 62 scoring matrix with a gappenalty of 12, a gap extend penalty of 4, and a frameshift gap penaltyof 5.

In some embodiments, the percent identity or similarity between aminoacid or nucleotide sequences can be determined using the algorithm of E.Meyers and W. Miller (1989, Cabios, 4: 11-17) which has beenincorporated into the ALIGN program (version 2.0), using a PAM120 weightresidue table, a gap length penalty of 12 and a gap penalty of 4.

The nucleic acid and protein sequences described herein can be used as a“query sequence” to perform a search against public databases to, forexample, identify other family members or related sequences. Suchsearches can be performed using the NBLAST and XBLAST programs (version2.0) of Altschul, et al., (1990, J. Mol. Biol, 215: 403-10). BLASTnucleotide searches can be performed with the NBLAST program, score=100,wordlength=12 to obtain nucleotide sequences homologous to 53010 nucleicacid molecules of the invention. BLAST protein searches can be performedwith the XBLAST program, score=50, wordlength=3 to obtain amino acidsequences homologous to 53010 protein molecules of the invention. Toobtain gapped alignments for comparison purposes, Gapped BLAST can beutilized as described in Altschul et al., (1997, Nucleic Acids Res, 25:3389-3402). When utilizing BLAST and Gapped BLAST programs, the defaultparameters of the respective programs (e.g., XBLAST and NBLAST) can beused.

Corresponding IRS biomarker polynucleotides also include nucleic acidsequences that hybridize to reference IRS biomarker polynucleotides, orto their complements, under stringency conditions described below. Asused herein, the term “hybridizes under low stringency, mediumstringency, high stringency, or very high stringency conditions”describes conditions for hybridization and washing. “Hybridization” isused herein to denote the pairing of complementary nucleotide sequencesto produce a DNA-DNA hybrid or a DNA-RNA hybrid. Complementary basesequences are those sequences that are related by the base-pairingrules. In DNA, A pairs with T and C pairs with G. In RNA, U pairs with Aand C pairs with G. In this regard, the terms “match” and “mismatch” asused herein refer to the hybridization potential of paired nucleotidesin complementary nucleic acid strands. Matched nucleotides hybridizeefficiently, such as the classical A-T and G-C base pair mentionedabove. Mismatches are other combinations of nucleotides that do nothybridize efficiently.

Guidance for performing hybridization reactions can be found in Ausubelet al., (1998, supra), Sections 6.3.1-6.3.6. Aqueous and non-aqueousmethods are described in that reference and either can be used.Reference herein to low stringency conditions include and encompass fromat least about 1% v/v to at least about 15% v/v formamide and from atleast about 1 M to at least about 2 M salt for hybridization at 42° C.,and at least about 1 M to at least about 2 M salt for washing at 42° C.Low stringency conditions also may include 1% Bovine Serum Albumin(BSA), 1 mM EDTA, 0.5 M NaHPO₄ (pH 7.2), 7% SDS for hybridization at 65°C., and (i) 2×SSC, 0.1% SDS; or (ii) 0.5% BSA, 1 mM EDTA, 40 mM NaHPO₄(pH 7.2), 5% SDS for washing at room temperature. One embodiment of lowstringency conditions includes hybridization in 6× sodiumchloride/sodium citrate (SSC) at about 45 C, followed by two washes in0.2×SSC, 0.1% SDS at least at 50° C. (the temperature of the washes canbe increased to 55° C. for low stringency conditions). Medium stringencyconditions include and encompass from at least about 16% v/v to at leastabout 30% v/v formamide and from at least about 0.5 M to at least about0.9 M salt for hybridization at 42° C., and at least about 0.1 M to atleast about 0.2 M salt for washing at 55° C. Medium stringencyconditions also may include 1% Bovine Serum Albumin (BSA), 1 mM EDTA,0.5 M NaHPO₄ (pH 7.2), 7% SDS for hybridization at 65° C., and (i)2×SSC, 0.1% SDS; or (ii) 0.5% BSA, 1 mM EDTA, 40 mM NaHPO₄ (pH 7.2), 5%SDS for washing at 60-65° C. One embodiment of medium stringencyconditions includes hybridizing in 6×SSC at about 45 C, followed by oneor more washes in 0.2×SSC, 0.1% SDS at 60° C. High stringency conditionsinclude and encompass from at least about 31% v/v to at least about 50%v/v formamide and from about 0.01 M to about 0.15 M salt forhybridization at 42° C., and about 0.01 M to about 0.02 M salt forwashing at 55° C. High stringency conditions also may include 1% BSA, 1mM EDTA, 0.5 M NaHPO₄ (pH 7.2), 7% SDS for hybridization at 65° C., and(i) 0.2×SSC, 0.1% SDS; or (ii) 0.5% BSA, 1 mM EDTA, 40 mM NaHPO₄ (pH7.2), 1% SDS for washing at a temperature in excess of 65° C. Oneembodiment of high stringency conditions includes hybridizing in 6×SSCat about 45 C, followed by one or more washes in 0.2×SSC, 0.1% SDS at65° C.

In certain embodiments, a corresponding IRS biomarker polynucleotide isone that hybridizes to a disclosed nucleotide sequence under very highstringency conditions. One embodiment of very high stringency conditionsincludes hybridizing 0.5 M sodium phosphate, 7% SDS at 65° C., followedby one or more washes at 0.2×SSC, 1% SDS at 65° C.

Other stringency conditions are well known in the art and a skilledaddressee will recognize that various factors can be manipulated tooptimize the specificity of the hybridization. Optimization of thestringency of the final washes can serve to ensure a high degree ofhybridization. For detailed examples, see Ausubel et al., supra at pages2.10.1 to 2.10.16 and Sambrook et al. (1989, supra) at sections 1.101 to1.104.

Thus, in some embodiments, IRS biomarker levels in control groups asbroadly defined above and elsewhere herein are used to generate aprofile of IRS biomarker levels reflecting difference between levels intwo control groups as described above and elsewhere herein. Thus, aparticular IRS biomarker may be more abundant or less abundant in onecontrol group as compared to another control group. The data may berepresented as an overall signature score or the profile may berepresented as a barcode or other graphical representation to facilitateanalysis or diagnosis or determination of likelihood. The IRS biomarkerlevels from a test subject may be represented in the same way and thesimilarity with the signature score or level of “fit” to a signaturebarcode or other graphical representation may be determined. In otherembodiments, the levels of a particular IRS biomarker are analysed and adownward or an upward trend in IRS biomarker level determined.

In some embodiments, the individual level of an IRS biomarker in a firstcontrol group (e.g., a control group selected from healthy conditioncontrol group, SIRS control group, inSIRS control group, ipSIRS controlgroup, or ipSIRS stage control group) is at least 101%, 102%, 103%,104%, 105%, 106%, 107% 108%, 109%, 110%, 120%, 130%, 140%, 150%, 160%,170%, 180%, 190%, 200%, 300%, 400%, 500%, 600%, 700%, 800%, 900% or1000% (i.e. an increased or higher level), or no more than about 99%,98%, 97%, 96%, 95%, 94%, 93%, 92%, 91%, 90%, 80%, 70%, 60%, 50%, 40%,30%, 20%, 10%, 5%, 4%, 3%, 2%, 1%, 0.5%, 0.1%, 0.01%, 0.001% or 0.0001%(i.e. a decreased or lower level) of the level of a corresponding IRSbiomarker in a second control group (e.g., a control group selected fromhealthy condition control group, SIRS control group, inSIRS controlgroup, ipSIRS control group, or ipSIRS stage control group, illustrativeexamples of which include “mild sepsis control group, severe sepsiscontrol group, and septic shock control group, which is different fromthe first control group).

An IRS biomarker profile provides a compositional analysis (e.g.,concentration or mole percentage (%) of the IRS biomarker) in which twoor more, three or more, four or more, five or more, six or more, sevenor more, eight or more, nine or more, ten or more, twelve or more,fifteen or more, twenty or more, fifty or more, one-hundred or more or agreater number of IRS biomarkers are evaluated.

The IRS biomarker profile can be quantitative, semi-quantitative and/orqualitative. For example, the IRS biomarker profile can evaluate thepresence or absence of an IRS biomarker, can evaluate the presence of anIRS biomarker(s) above or below a particular threshold, and/or canevaluate the relative or absolute amount of an IRS biomarker(s). Inparticular embodiments, a ratio among two, three, four or more IRSbiomarkers is determined (see Example 6 and Tables 16-21 for examples ofthe use of 2-gene ratios in separating various inSIRS and ipSIRSconditions). Changes or perturbations in IRS biomarker ratios can beadvantageous in indicating where there are blocks (or releases of suchblocks) or other alterations in cellular pathways associated with an IRScondition, response to treatment, development of side effects, and thelike.

IRS biomarkers may be quantified or detected using any suitabletechnique including nucleic acid- and protein-based assays.

In illustrative nucleic acid-based assays, nucleic acid is isolated fromcells contained in the biological sample according to standardmethodologies (Sambrook, et al., 1989, supra; and Ausubel et al., 1994,supra). The nucleic acid is typically fractionated (e.g., poly A⁺ RNA)or whole cell RNA. Where RNA is used as the subject of detection, it maybe desired to convert the RNA to a complementary DNA. In someembodiments, the nucleic acid is amplified by a template-dependentnucleic acid amplification technique. A number of template dependentprocesses are available to amplify the IRS biomarker sequences presentin a given template sample. An exemplary nucleic acid amplificationtechnique is the polymerase chain reaction (referred to as PCR), whichis described in detail in U.S. Pat. Nos. 4,683,195, 4,683,202 and4,800,159, Ausubel et al. (supra), and in Innis et al., (“PCRProtocols”, Academic Press, Inc., San Diego Calif., 1990). Briefly, inPCR, two primer sequences are prepared that are complementary to regionson opposite complementary strands of the biomarker sequence. An excessof deoxynucleotide triphosphates are added to a reaction mixture alongwith a DNA polymerase, e.g., Taq polymerase. If a cognate IRS biomarkersequence is present in a sample, the primers will bind to the biomarkerand the polymerase will cause the primers to be extended along thebiomarker sequence by adding on nucleotides. By raising and lowering thetemperature of the reaction mixture, the extended primers willdissociate from the biomarker to form reaction products, excess primerswill bind to the biomarker and to the reaction products and the processis repeated. A reverse transcriptase PCR amplification procedure may beperformed in order to quantify the amount of mRNA amplified. Methods ofreverse transcribing RNA into cDNA are well known and described inSambrook et al., 1989, supra. Alternative methods for reversetranscription utilize thermostable, RNA-dependent DNA polymerases. Thesemethods are described in WO 90/07641. Polymerase chain reactionmethodologies are well known in the art.

In certain advantageous embodiments, the template-dependentamplification involves quantification of transcripts in real-time. Forexample, RNA or DNA may be quantified using the Real-Time PCR technique(Higuchi, 1992, et al., Biotechnology 10: 413-417). By determining theconcentration of the amplified products of the target DNA in PCRreactions that have completed the same number of cycles and are in theirlinear ranges, it is possible to determine the relative concentrationsof the specific target sequence in the original DNA mixture. If the DNAmixtures are cDNAs synthesized from RNAs isolated from different tissuesor cells, the relative abundance of the specific mRNA from which thetarget sequence was derived can be determined for the respective tissuesor cells. This direct proportionality between the concentration of thePCR products and the relative mRNA abundance is only true in the linearrange of the PCR reaction. The final concentration of the target DNA inthe plateau portion of the curve is determined by the availability ofreagents in the reaction mix and is independent of the originalconcentration of target DNA. In specific embodiments, multiplexed,tandem PCR (MT-PCR) is employed, which uses a two-step process for geneexpression profiling from small quantities of RNA or DNA, as describedfor example in US Pat. Appl. Pub. No. 20070190540. In the first step,RNA is converted into cDNA and amplified using multiplexed gene specificprimers. In the second step each individual gene is quantitated by realtime PCR.

In certain embodiments, target nucleic acids are quantified usingblotting techniques, which are well known to those of skill in the art.Southern blotting involves the use of DNA as a target, whereas Northernblotting involves the use of RNA as a target.

Each provides different types of information, although cDNA blotting isanalogous, in many aspects, to blotting or RNA species. Briefly, a probeis used to target a DNA or RNA species that has been immobilized on asuitable matrix, often a filter of nitrocellulose. The different speciesshould be spatially separated to facilitate analysis. This often isaccomplished by gel electrophoresis of nucleic acid species followed by“blotting” on to the filter. Subsequently, the blotted target isincubated with a probe (usually labelled) under conditions that promotedenaturation and rehybridisation. Because the probe is designed to basepair with the target, the probe will bind a portion of the targetsequence under renaturing conditions. Unbound probe is then removed, anddetection is accomplished as described above. Followingdetection/quantification, one may compare the results seen in a givensubject with a control reaction or a statistically significant referencegroup or population of control subjects as defined herein. In this way,it is possible to correlate the amount of a IRS biomarker nucleic aciddetected with the progression or severity of the disease.

Also contemplated are biochip-based technologies such as those describedby Hacia et al. (1996, Nature Genetics 14: 441-447) and Shoemaker et al.(1996, Nature Genetics 14: 450-456). Briefly, these techniques involvequantitative methods for analysing large numbers of genes rapidly andaccurately. By tagging genes with oligonucleotides or using fixed probearrays, one can employ biochip technology to segregate target moleculesas high-density arrays and screen these molecules on the basis ofhybridization. See also Pease et al. (1994, Proc. Natl. Acad. Sci.U.S.A. 91: 5022-5026); Fodor et al. (1991, Science 251: 767-773).Briefly, nucleic acid probes to IRS biomarker polynucleotides are madeand attached to biochips to be used in screening and diagnostic methods,as outlined herein. The nucleic acid probes attached to the biochip aredesigned to be substantially complementary to specific expressed IRSbiomarker nucleic acids, i.e., the target sequence (either the targetsequence of the sample or to other probe sequences, for example insandwich assays), such that hybridization of the target sequence and theprobes of the present invention occur. This complementarity need not beperfect; there may be any number of base pair mismatches, which willinterfere with hybridization between the target sequence and the nucleicacid probes of the present invention. However, if the number ofmismatches is so great that no hybridization can occur under even theleast stringent of hybridization conditions, the sequence is not acomplementary target sequence. In certain embodiments, more than oneprobe per sequence is used, with either overlapping probes or probes todifferent sections of the target being used. That is, two, three, fouror more probes, with three being desirable, are used to build in aredundancy for a particular target. The probes can be overlapping (i.e.have some sequence in common), or separate.

In an illustrative biochip analysis, oligonucleotide probes on thebiochip are exposed to or contacted with a nucleic acid sample suspectedof containing one or more IRS biomarker polynucleotides under conditionsfavouring specific hybridization. Sample extracts of DNA or RNA, eithersingle or double-stranded, may be prepared from fluid suspensions ofbiological materials, or by grinding biological materials, or followinga cell lysis step which includes, but is not limited to, lysis effectedby treatment with SDS (or other detergents), osmotic shock, guanidiniumisothiocyanate and lysozyme. Suitable DNA, which may be used in themethod of the invention, includes cDNA. Such DNA may be prepared by anyone of a number of commonly used protocols as for example described inAusubel, et al., 1994, supra, and Sambrook, et al., et al., 1989, supra.

Suitable RNA, which may be used in the method of the invention, includesmessenger RNA, complementary RNA transcribed from DNA (cRNA) or genomicor subgenomic RNA. Such RNA may be prepared using standard protocols asfor example described in the relevant sections of Ausubel, et al. 1994,supra and Sambrook, et al. 1989, supra).

cDNA may be fragmented, for example, by sonication or by treatment withrestriction endonucleases. Suitably, cDNA is fragmented such thatresultant DNA fragments are of a length greater than the length of theimmobilized oligonucleotide probe(s) but small enough to allow rapidaccess thereto under suitable hybridization conditions. Alternatively,fragments of cDNA may be selected and amplified using a suitablenucleotide amplification technique, as described for example above,involving appropriate random or specific primers.

Usually the target IRS biomarker polynucleotides are detectably labelledso that their hybridization to individual probes can be determined. Thetarget polynucleotides are typically detectably labelled with a reportermolecule illustrative examples of which include chromogens, catalysts,enzymes, fluorochromes, chemiluminescent molecules, bioluminescentmolecules, lanthanide ions (e.g., Eu³⁴), a radioisotope and a directvisual label. In the case of a direct visual label, use may be made of acolloidal metallic or non-metallic particle, a dye particle, an enzymeor a substrate, an organic polymer, a latex particle, a liposome, orother vesicle containing a signal producing substance and the like.Illustrative labels of this type include large colloids, for example,metal colloids such as those from gold, selenium, silver, tin andtitanium oxide. In some embodiments in which an enzyme is used as adirect visual label, biotinylated bases are incorporated into a targetpolynucleotide.

The hybrid-forming step can be performed under suitable conditions forhybridizing oligonucleotide probes to test nucleic acid including DNA orRNA. In this regard, reference may be made, for example, to NUCLEIC ACIDHYBRIDIZATION, A PRACTICAL APPROACH (Homes and Higgins, eds.) (IRLpress, Washington D.C., 1985). In general, whether hybridization takesplace is influenced by the length of the oligonucleotide probe and thepolynucleotide sequence under test, the pH, the temperature, theconcentration of mono- and divalent cations, the proportion of G and Cnucleotides in the hybrid-forming region, the viscosity of the mediumand the possible presence of denaturants. Such variables also influencethe time required for hybridization. The preferred conditions willtherefore depend upon the particular application. Such empiricalconditions, however, can be routinely determined without undueexperimentation.

After the hybrid-forming step, the probes are washed to remove anyunbound nucleic acid with a hybridization buffer. This washing stepleaves only bound target polynucleotides. The probes are then examinedto identify which probes have hybridized to a target polynucleotide.

The hybridization reactions are then detected to determine which of theprobes has hybridized to a corresponding target sequence. Depending onthe nature of the reporter molecule associated with a targetpolynucleotide, a signal may be instrumentally detected by irradiating afluorescent label with light and detecting fluorescence in afluorimeter; by providing for an enzyme system to produce a dye whichcould be detected using a spectrophotometer; or detection of a dyeparticle or a coloured colloidal metallic or non metallic particle usinga reflectometer; in the case of using a radioactive label orchemiluminescent molecule employing a radiation counter orautoradiography. Accordingly, a detection means may be adapted to detector scan light associated with the label which light may includefluorescent, luminescent, focussed beam or laser light. In such a case,a charge couple device (CCD) or a photocell can be used to scan foremission of light from a probe:target polynucleotide hybrid from eachlocation in the micro-array and record the data directly in a digitalcomputer. In some cases, electronic detection of the signal may not benecessary. For example, with enzymatically generated colour spotsassociated with nucleic acid array format, visual examination of thearray will allow interpretation of the pattern on the array. In the caseof a nucleic acid array, the detection means is suitably interfaced withpattern recognition software to convert the pattern of signals from thearray into a plain language genetic profile. In certain embodiments,oligonucleotide probes specific for different IRS biomarkerpolynucleotides are in the form of a nucleic acid array and detection ofa signal generated from a reporter molecule on the array is performedusing a ‘chip reader’. A detection system that can be used by a ‘chipreader’ is described for example by Pirrung et al (U.S. Pat. No.5,143,854). The chip reader will typically also incorporate some signalprocessing to determine whether the signal at a particular arrayposition or feature is a true positive or maybe a spurious signal.Exemplary chip readers are described for example by Fodor et al (U.S.Pat. No. 5,925,525). Alternatively, when the array is made using amixture of individually addressable kinds of labelled microbeads, thereaction may be detected using flow cytometry.

In other embodiments, IRS biomarker protein levels are assayed usingprotein-based assays known in the art. For example, when an IRSbiomarker protein is an enzyme, the protein can be quantified based uponits catalytic activity or based upon the number of molecules of theprotein contained in a sample. Antibody-based techniques may be employedincluding, for example, immunoassays, such as the enzyme-linkedimmunosorbent assay (ELISA) and the radioimmunoassay (RIA).

In specific embodiments, protein-capture arrays that permit simultaneousdetection and/or quantification of a large number of proteins areemployed. For example, low-density protein arrays on filter membranes,such as the universal protein array system (Ge, 2000 Nucleic Acids Res.28(2):e3) allow imaging of arrayed antigens using standard ELISAtechniques and a scanning charge-coupled device (CCD) detector.Immuno-sensor arrays have also been developed that enable thesimultaneous detection of clinical analytes. It is now possible usingprotein arrays, to profile protein expression in bodily fluids, such asin sera of healthy or diseased subjects, as well as in subjects pre- andpost-drug treatment.

Exemplary protein capture arrays include arrays comprising spatiallyaddressed antigen-binding molecules, commonly referred to as antibodyarrays, which can facilitate extensive parallel analysis of numerousproteins defining a proteome or subproteome. Antibody arrays have beenshown to have the required properties of specificity and acceptablebackground, and some are available commercially (e.g., BD Biosciences,Clontech, BioRad and Sigma). Various methods for the preparation ofantibody arrays have been reported (see, e.g., Lopez et al., 2003 J.Chromatogr. B 787:19-27; Cahill, 2000 Trends in Biotechnology 7:47-51;U.S. Pat. App. Pub. 2002/0055186; U.S. Pat. App. Pub. 2003/0003599; PCTpublication WO 03/062444; PCT publication WO 03/077851; PCT publicationWO 02/59601; PCT publication WO 02/39120; PCT publication WO 01/79849;PCT publication WO 99/39210). The antigen-binding molecules of sucharrays may recognise at least a subset of proteins expressed by a cellor population of cells, illustrative examples of which include growthfactor receptors, hormone receptors, neurotransmitter receptors,catecholamine receptors, amino acid derivative receptors, cytokinereceptors, extracellular matrix receptors, antibodies, lectins,cytokines, serpins, proteases, kinases, phosphatases, ras-like GTPases,hydrolases, steroid hormone receptors, transcription factors, heat-shocktranscription factors, DNA-binding proteins, zinc-finger proteins,leucine-zipper proteins, homeodomain proteins, intracellular signaltransduction modulators and effectors, apoptosis-related factors, DNAsynthesis factors, DNA repair factors, DNA recombination factors andcell-surface antigens.

Individual spatially distinct protein-capture agents are typicallyattached to a support surface, which is generally planar or contoured.Common physical supports include glass slides, silicon, microwells,nitrocellulose or PVDF membranes, and magnetic and other microbeads.

Particles in suspension can also be used as the basis of arrays,providing they are coded for identification; systems include colourcoding for microbeads (e.g., available from Luminex, Bio-Rad andNanomics Biosystems) and semiconductor nanocrystals (e.g., QDots™,available from Quantum Dots), and barcoding for beads (UltraPlex™,available from Smartbeads) and multimetal microrods (Nanobarcodes™particles, available from Surromed). Beads can also be assembled intoplanar arrays on semiconductor chips (e.g., available from LEAPStechnology and BioArray Solutions). Where particles are used, individualprotein-capture agents are typically attached to an individual particleto provide the spatial definition or separation of the array. Theparticles may then be assayed separately, but in parallel, in acompartmentalized way, for example in the wells of a microtiter plate orin separate test tubes.

In operation, a protein sample, which is optionally fragmented to formpeptide fragments (see, e.g., U.S. Pat. App. Pub. 2002/0055186), isdelivered to a protein-capture array under conditions suitable forprotein or peptide binding, and the array is washed to remove unbound ornon-specifically bound components of the sample from the array. Next,the presence or amount of protein or peptide bound to each feature ofthe array is detected using a suitable detection system. The amount ofprotein bound to a feature of the array may be determined relative tothe amount of a second protein bound to a second feature of the array.In certain embodiments, the amount of the second protein in the sampleis already known or known to be invariant.

For analysing differential expression of proteins between two cells orcell populations, a protein sample of a first cell or population ofcells is delivered to the array under conditions suitable for proteinbinding. In an analogous manner, a protein sample of a second cell orpopulation of cells to a second array is delivered to a second arraythat is identical to the first array. Both arrays are then washed toremove unbound or non-specifically bound components of the sample fromthe arrays. In a final step, the amounts of protein remaining bound tothe features of the first array are compared to the amounts of proteinremaining bound to the corresponding features of the second array. Todetermine the differential protein expression pattern of the two cellsor populations of cells, the amount of protein bound to individualfeatures of the first array is subtracted from the amount of proteinbound to the corresponding features of the second array.

All the essential materials and reagents required for detecting andquantifying IRS biomarker expression products may be assembled togetherin a kit, which is encompassed by the present invention. The kits mayalso optionally include appropriate reagents for detection of labels,positive and negative controls, washing solutions, blotting membranes,microtiter plates dilution buffers and the like. For example, a nucleicacid-based detection kit may include (i) an IRS biomarker polynucleotide(which may be used as a positive control), (ii) a primer or probe thatspecifically hybridizes to an IRS biomarker polynucleotide. Alsoincluded may be enzymes suitable for amplifying nucleic acids includingvarious polymerases (Reverse Transcriptase, Taq, Sequenase™, DNA ligaseetc. depending on the nucleic acid amplification technique employed),deoxynucleotides and buffers to provide the necessary reaction mixturefor amplification. Such kits also generally will comprise, in suitablemeans, distinct containers for each individual reagent and enzyme aswell as for each primer or probe. Alternatively, a protein-baseddetection kit may include (i) an IRS biomarker polypeptide (which may beused as a positive control), (ii) an antibody that binds specifically toan IRS biomarker polypeptide. The kit can also feature various devices(e.g., one or more) and reagents (e.g., one or more) for performing oneof the assays described herein; and/or printed instructions for usingthe kit to quantify the expression of an IRS biomarker gene.

In some embodiments, the methods and kits comprise or enable: comparingthe level of at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 etc.) IRSbiomarker in the subject's sample IRS profile to the level of acorresponding IRS biomarker in a reference IRS biomarker profile from atleast one control subject or group selected from a healthy controlsubject or group (hereafter referred to as a “reference healthy IRSbiomarker profile”), a SIRS control subject or group (hereafter referredto as a “reference SIRS IRS biomarker profile”), an inSIRS controlsubject or group (hereafter referred to as a “reference inSIRS IRSbiomarker profile”), an ipSIRS control subject or group (hereafterreferred to as a “reference ipSIRS IRS biomarker profile”) and a controlsubject or group with a particular stage of ipSIRS (hereafter referredto as a “reference ipSIRS stage IRS biomarker profile”), wherein asimilarity between the level of the at least one IRS biomarker in thesample IRS biomarker profile and the level of the corresponding IRSbiomarker in the reference healthy IRS biomarker profile identifies thatthe subject has an IRS biomarker profile that correlates with thepresence of a healthy condition, or alternatively the absence of inSIRS,ipSIRS, or a particular stage of ipSIRS, wherein a similarity betweenthe level of the at least one IRS biomarker in the sample IRS biomarkerprofile and the level of the corresponding IRS biomarker in the SIRS IRSbiomarker profile identifies that the subject has an IRS biomarkerprofile that correlates with the presence of inSIRS or ipSIRS, oralternatively the absence of a healthy condition, wherein a similaritybetween the level of the at least one IRS biomarker in the sample IRSbiomarker profile and the level of the corresponding IRS biomarker inthe inSIRS IRS biomarker profile identifies that the subject has an IRSbiomarker profile that correlates with the presence of inSIRS, oralternatively the absence of a healthy condition, ipSIRS, or aparticular stage of ipSIRS, wherein a similarity between the level ofthe at least one IRS biomarker in the sample IRS biomarker profile andthe level of the corresponding IRS biomarker in the ipSIRS IRS biomarkerprofile identifies that the subject has an IRS biomarker profile thatcorrelates with the presence of ipSIRS, or alternatively the absence ofa healthy condition or inSIRS, and wherein a similarity between thelevel of the at least one IRS biomarker in the sample IRS biomarkerprofile and the level of the corresponding IRS biomarker in the ipSIRSstage IRS biomarker profile identifies that the subject has an IRSbiomarker profile that correlates with the presence of a particularstage of ipSIRS, or alternatively the absence of a healthy condition orinSIRS.

A subset of the instantly disclosed IRS biomarkers has been identifiedas being useful for assisting in distinguishing between healthy subjectsand unhealthy subjects that have SIRS (i.e., sick subjects with eitherinSIRS or ipSIRS). Thus, in some embodiments, the methods and kitsinvolve determining the likelihood that SIRS or a healthy condition(e.g., a normal condition or a condition in which SIRS is absent) ispresent or absent in a subject. These methods and kits generallycomprise or involve: 1) providing a correlation of a reference IRSbiomarker profile with the presence or absence of SIRS or the healthycondition, wherein the reference biomarker profile evaluates at leastone (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 etc.) IRS biomarker selectedfrom CD177, CLEC4D, BMX, VNN1, GPR84, ARG1, IL18R1, ERLIN1, IMP3, TLR5,UBE2J1, GPR56, FCGR1A, SLC1A3, SLC37A3, FAIM3, C3AR1, RNASE2, TNFAIP6,GNLY, OMG, FAR2, OLAH, CAMK4, METTL7B, B3GNT5, CLEC4E, MMP9, KREMEN1,GALNT3, PTGDR, TDRD9, GK3P, FKBP5, STOM, SMPDL3A, PFKFB2, ANKRD34B,SGMS2, DNAJC13, LRRN3, SH2D1B, C1orf161, HIST1H4C, IFI16, ACER3, PLB1,C9orf72, HMGB2, KLRK1, C7orf53, GOT2, TCN1, DSE, CCR3, CRIP1, ITK,KLRF1, TGFBR1, GSR, HIST1H4E, HPGD, FRMD3, ABCA13, C11orf82, PPP2R5A,BPI, CASS4, AP3B2, ODZ1, TMTC1, ADM, FGFBP2, HSPC159, HLA-DRA, HIST1H3I,TMEM144, MRPL41, FOLR3, PICALM, SH3PXD2B, DDAH2, HLA-DPB1, KPNA5,PHOSPHO1, TPST1, EIF2AK2, OR9A2, OLFM4, CD163, CDA, CHI3L1, MTHFS, CLU,ANAPC11, JUP, PMAIP1, GIMAP7, KLRD1, CCR1, CD274, EFCAB2, SUCNR1,KCNMA1, LGALS2, SLC11A1, FOXD4L3, VAMP2, ITGA4, LHFP, PRR13, FFAR2,B3GAT3, EAF2, HPSE, CLC, TLR10, CCR4, HIST1H3A, CENPK, DPH3, HLA-DPA1,ATP13A3, DNAJC9, S100B, HIST1H3J, 110, RPL17, C15orf54, LRRC70, IL5RA,PLA2G7, ECHDC3, HINT1, LCN2, PPIF, SLC15A2, PMS2CL, HIST1H2AA, CEACAM8,HSP90AB1, ABCG1, PDGFC, NPCDR1, PDK4, GAB2, WSB2, FAM118A, JKAMP,TREML1, PYHIN1, IRF4, ABCA1, DAAM2, ACPL2, RCBTB2, SAP30, THBS1,PCOLCE2, GPR65, NF-E4, LTF, LASS4, B4GALT3, RETN, TIMM10, IL1B, CLEC4A,SEC24A, RUNX2, LRRFIP1, CFD, EIF1AX, ZRANB1, SULF2, EXOSC4, CCDC125,LOC284757, ANKRD28, HIST1H2AJ, CD63, PLIN2, SON, HIST1H4L, KRTAP15-1,DLEU2, MYL9, FABP2, CD24, MACF1, GSTO1, RRP12, AIG1, RASA4, FBXL13,PDE3B, CCRL2, C1orf128, E2F6, IL1RL1, CEACAM6, CYP4F3, 199, TAAR1,TSHZ2, PLB1, UBE2F (where if a gene name is not provided then a SEQ IDNO. is provided); (2) obtaining a sample IRS biomarker profile from thesubject, which evaluates for an individual IRS biomarker in thereference IRS biomarker profile a corresponding IRS biomarker, and (3)determining a likelihood of the subject having or not having the healthycondition or SIRS based on the sample IRS biomarker profile and thereference IRS biomarker profile.

In illustrative examples of this type, a reference healthy condition IRSbiomarker profile comprises at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8,9, 10 etc.) IRS biomarker that is downregulated or underexpressedrelative to a reference SIRS IRS biomarker profile, illustrativeexamples of which include: GNLY, GPR56, KLRF1, HIST1H2AJ, HIST1H4C,KLRK1, CHI3L1, SH2D1B, PTGDR, CAMK4, FAIM3, CRIP1, CLC, HLA-DPB1,FGFBP2, HIST1H3J, IMP3, ITK, HIST1H3I, LRRN3, KLRD1, PHOSPHO1, CCR3,HIST1H4E, MRPL41, HIST1H3A, HLA-DRA, GIMAP7, KPNA5, CENPK, HLA-DPA1,HINT1, HIST1H4L, GOT2, DNAJC9, PLA2G7, CASS4, CFD, ITGA4, HSP90AB1,IL5RA, PMAIP1, LGALS2, SULF2, C1orf128, RPL17, EIF1AX, PYHIN1, S100B,PMS2CL, CCR4, C15orf54, VAMP2, ANAPC11, B3GAT3, E2F6, NPCDR1, FAM118A,PPIF, 199, JUP, B4GALT3, TIMM10, RUNX2, RASA4, SON, ABCG1, TSHZ2, IRF4,PDE3B, RRP12, LASS4 (where if a gene name is not provided then a SEQ IDNO. is provided).is provided).

In other illustrative examples, a reference healthy condition IRSbiomarker profile comprises at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8,9, 10 etc.) IRS biomarker that is upregulated or overexpressed relativeto a reference SIRS IRS biomarker profile, non-limiting examples ofwhich include: CD177, ARG1, VNN1, CLEC4D, GPR84, IL18R1, OLFM4, FCGR1A,RNASE2, TLR5, TNFAIP6, PFKFB2, C3AR1, TCN1, BMX, FKBP5, TDRD9, OLAH,ERLIN1, LCN2, MMP9, BPI, CEACAM8, CLEC4E, HPGD, CD274, GK3P, KREMEN1,ANKRD34B, SLC37A3, CD163, TMTC1, PLB1, UBE2J1, TPST1, B3GNT5, SMPDL3A,FAR2, ACER3, ODZ1, HMGB2, LTF, SGMS2, EIF2AK2, TMEM144, GALNT3, DNAJC13,IFI16, C11orf82, ABCA13, CD24, METTL7B, FOLR3, C7orf53, SLC1A3, DAAM2,HSPC159, OMG, CCR1, TREML1, STOM, CEACAM6, FOXD4L3, C9orf72, GSR, DSE,THBS1, SH3PXD2B, PDGFC, KCNMA1, PICALM, TLR10, PDK4, ADM, CLU, C1orf161,NF-E4, HPSE, FFAR2, PPP2R5A, CDA, NA, ATP13A3, ABCA1, TGFBR1, OR9A2,EFCAB2, EAF2, AP3B2, SLC15A2, ECHDC3, MTHFS, IL1B, WSB2, SUCNR1, DDAH2,CLEC4A, MACF1, MYL9, IL1RL1, EXOSC4, FBXL13, LOC284757, PRR13, DPH3,SLC11A1, FRMD3, ACPL2, PLB1, RETN, RCBTB2, CD63, CYP4F3, SEC24A, ZRANB1,CCDC125, PCOLCE2, JKAMP, LRRFIP1, GPR65, ANKRD28, LRRC70, AIG1, UBE2F,GAB2, CCRL2, SAP30, DLEU2, HIST1H2AA, GSTO1, PLIN2, LHFP, KRTAP15-1,TAAR1, FABP2 (where if a gene name is not provided then a SEQ ID NO. isprovided).

In still other illustrative examples, a reference healthy condition IRSbiomarker profile comprises: (1) at least one IRS biomarker that isdownregulated or underexpressed relative to a reference SIRS IRSbiomarker profile, as broadly described above and (2) at least one IRSbiomarker that is upregulated or overexpressed relative to a referenceSIRS IRS biomarker profile, as broadly described above.

The term “upregulated,” “overexpressed” and the like refer to an upwarddeviation in the level of expression of an IRS biomarker as compared toa baseline expression level of a corresponding IRS biomarker in acontrol sample.

The term “downregulated,” “underexpressed” and the like refer to adownward deviation in the level of expression of an IRS biomarker ascompared to a baseline expression level of a corresponding IRS biomarkerin a control sample.

Another subset of the instantly disclosed IRS biomarkers has beenidentified as being useful for assisting in distinguishing betweenhealthy subjects, inSIRS affected subjects and subjects having ipSIRS.Accordingly, in some embodiments, the methods and kits are useful fordetermining the likelihood that inSIRS, ipSIRS or a healthy condition(e.g., a normal condition or a condition in which SIRS is absent) ispresent or absent in a subject. These methods and kits generallycomprise or involve: 1) providing a correlation of a reference IRSbiomarker profile with the likelihood of having or not having inSIRS,ipSIRS or the healthy condition, wherein the reference biomarker profileevaluates at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 etc.) IRSbiomarker selected from PLACE, 132, INSIG1, CDS2, VOPP1, SLC39A9,B3GAT3, CD300A, OCR1, PTGER2, LGALS1, HIST1H4L, AMFR, SIAE, SLC39A8,TGFBR1, GAB2, MRPL41, TYMS, HIST1H3B, MPZL3, KIAA1257, OMG, HIST1H2BM,TDRD9, C22orf37, GALNT3, SYNE2, MGST3, HIST1H3I, LOC284757, TRAF3,HIST1H3C, STOM, C3AR1, KIAA0101, TNFRSF17, HAL, UBE2J1, GLT25D1, CD151,HSPB1, IMP3, PICALM, ACER3, IGL@, HIST1H2BJ, CASS4, KREMEN1, IRS2,APOLD1, RBP7, DNAJC13, ERGIC1, FSD1L, TLR5, TMEM62, SDHC, C9orf72, NP,KIAA0746, PMAIP1, DSE, SMPDL3A, DNAJC9, HIST1H3H, CDC26, CRIP1, FAR2,FRMD3, RGS2, METTL7B, CLEC4E, MME, ABCA13, PRR13, HIST1H4C, RRP12, GLDC,ECHDC3, IRF1, C7orf53, IGK@, RNASE2, FCGR1A, SAP30, PMS2CL, SLC11A1,AREG, PLB1, PPIF, GSR, NFXL1, AP3B2, DCTN5, RPL17, IGLV6-57, KLRF1,CHI3L1, ANKRD34B, OLFM4, CPM, CCDC125, GPR56, PPP1R2, 110, ACPL2,HIST1H3A, C7orf58, IRF4, ANAPC11, HIST1H3J, KLRD1, GPR84, ZRANB1, KDM6B,TPST1, HINT1, DAAM2, PTGDR, FKBP5, HSP90AB1, HPGD, IFI16, CD177,TAS2R31, CD163, B4GALT3, EIF1AX, CYP4F3, HIST1H2AA, LASS4 (where if agene name is not provided then a SEQ ID NO. is provided).; (2) obtaininga sample IRS biomarker profile from the subject, which evaluates for anindividual IRS biomarker in the reference IRS biomarker profile acorresponding IRS biomarker; and (3) determining a likelihood of thesubject having or not having inSIRS, ipSIRS or a healthy condition thecondition based on the sample IRS biomarker profile and the referenceIRS biomarker profile.

In illustrative examples of this type, a reference healthy condition IRSbiomarker profile comprises at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8,9, 10 etc.) IRS biomarker that is downregulated or underexpressedrelative to a reference inSIRS IRS biomarker profile, representativeexamples of which include: CD177, CLEC4E, FKBP5, CD163, TPST1, DAAM2,GPR84, FCGR1A, IFI16, RNASE2, TLR5, ECHDC3, OCR1, MME, LOC284757, 110,C3AR1, HAL, PRR13, ACPL2, SLC11A1, CYP4F3, SAP30, OLFM4, ZRANB1, GAB2,CCDC125, KREMEN1, UBE2J1, AREG, FAR2, CPM, PLB1, ERGIC1, RGS2, 132,HPGD, ANKRD34B, TDRD9, DNAJC13, GALNT3, IRS2, HIST1H2AA, RBP7, KDM6B,ACER3, MPZL3, KIAA1257, C7orf53, C9orf72, STOM, METTL7B, SMPDL3A, GSR,SYNE2, OMG, DSE, PICALM, ABCA13, PPP1R2, TGFBR1, AP3B2, FRMD3 (where ifa gene name is not provided then a SEQ ID NO. is provided).

In other illustrative examples, a reference healthy condition IRSbiomarker profile comprises at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8,9, 10 etc.) IRS biomarker that is upregulated or overexpressed, relativeto a reference inSIRS IRS biomarker profile, illustrative examples ofwhich include: SIAE, FSD1L, GLDC, HSPB1, HIST1H2BJ, CDS2, CASS4, DCTN5,SLC39A9, CDC26, LGALS1, CD151, NP, TYMS, IGLV6-57, TMEM62, CD300A,LASS4, GLT25D1, IRF1, AMFR, IGL@, NFXL1, SLC39A8, APOLD1, TNFRSF17,KIAA0101, C22orf37, VOPP1, KLRD1, TRAF3, RRP12, PTGER2, KIAA0746, MGST3,CHI3L1, TAS2R31, SDHC, IRF4, INSIG1, PPIF, B4GALT3, ANAPC11, PLAC8,HIST1H2BM, KLRF1, B3GAT3, C7orf58, PMS2CL, PTGDR, RPL17, EIF1AX, PMAIP1,HIST1H3B, IGK@, HINT1, HSP90AB1, GPR56, HIST1H3H, HIST1H3A, IMP3,DNAJC9, MRPL41, HIST1H3J, HIST1H3C, HIST1H3I, HIST1H4L, CRIP1, HIST1H4C(where if a gene name is not provided then a SEQ ID NO. is provided).

In still other illustrative examples, a reference healthy condition IRSbiomarker profile comprises: (1) at least one IRS biomarker that isdownregulated or underexpressed relative to a reference inSIRS IRSbiomarker profile, as broadly described above and (2) at least one IRSbiomarker that is upregulated or overexpressed relative to a referenceinSIRS IRS biomarker profile, as broadly described above.

In other illustrative examples, a reference inSIRS IRS biomarker profilecomprises at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 etc.) IRSbiomarker that is downregulated or underexpressed relative to areference ipSIRS IRS biomarker profile, representative examples of whichinclude: OLFM4, PLAC8, HIST1H4L, HIST1H3C, TDRD9, IGK@, HIST1H3B,HIST1H2BM, HPGD, GPR84, TLR5, SMPDL3A, CD177, HIST1H3I, C3AR1, DNAJC9,ABCA13, ANKRD34B, RNASE2, FCGR1A, HIST1H3H, KIAA0746, ACER3, SDHC,CRIP1, IGLV6-57, PLB1, MRPL41, HIST1H4C, SLC39A8, NP, NFXL1, PTGER2,TYMS, LGALS1, C7orf58, CD151, KREMEN1, AMFR, METTL7B, TNFRSF17,HSP90AB1, VOPP1, GLT25D1, GALNT3, OMG, SIAE, FAR2, C7orf53, DNAJC13,HIST1H2BJ, KIAA0101, HSPB1, UBE2J1, HIST1H3J, CDS2, MGST3, PICALM,HINT1, SLC39A9, STOM, TRAF3, INSIG1, AP3B2, B3GAT3, CD300A, TGFBR1,HIST1H3A, PMAIP1, DSE, TMEM62, IGL@, IRF4, GSR, IRF1, EIF1AX, C9orf72,PMS2CL, C22orf37, FRMD3, IMP3, RPL17, FSD1L, APOLD1, B4GALT3, DCTN5,PPIF, CDC26, TAS2R31, RRP12, ANAPC11, GLDC, LASS4 (where if a gene nameis not provided then a SEQ ID NO. is provided).

In yet other illustrative examples, a reference inSIRS IRS biomarkerprofile comprises at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10etc.) IRS biomarker that is upregulated or overexpressed, relative to areference ipSIRS IRS biomarker profile, non-limiting examples of whichinclude: HIST1H2AA, IFI16, PPP1R2, CCDC125, ZRANB1, SLC11A1, GPR56, 110,KDM6B, GAB2, CYP4F3, RGS2, KIAA1257, CPM, ACPL2, PRR13, ERGIC1, PTGDR,IRS2, MPZL3, AREG, SAP30, RBP7, CASS4, FKBP5, SYNE2, KLRD1, 132, KLRF1,LOC284757, HAL, TPST1, ECHDC3, CD163, CLEC4E, DAAM2, CHI3L1, MME, OCR1(where if a gene name is not provided then a SEQ ID NO. is provided).

In still other illustrative examples, a reference inSIRS IRS biomarkerprofile comprises: (1) at least one IRS biomarker that is downregulatedor underexpressed relative to a reference ipSIRS IRS biomarker profile,as broadly described above and (2) at least one IRS biomarker that isupregulated or overexpressed relative to a reference ipSIRS IRSbiomarker profile, as broadly described above.

In other illustrative examples, a reference ipSIRS IRS biomarker profilecomprises at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 etc.) IRSbiomarker that is downregulated or underexpressed relative to areference healthy condition IRS biomarker profile, representativeexamples of which include: GNLY, GPR56, CHI3L1, KLRF1, KLRK1, PTGDR,SH2D1B, HIST1H2AJ, FAIM3, HLA-DPB1, CAMK4, FGFBP2, KLRD1, CLC, PHOSPHO1,HIST1H4C, ITK, LRRN3, CCR3, CRIP1, IMP3, HIST1H3J, HIST1H4E, HLA-DRA,PLA2G7, GIMAP7, HLA-DPA1, CASS4, HIST1H3I, KPNA5, CENPK, SULF2,KIAA1324, HIST1H3A, CFD, C1orf128, RPIA, MRPL41, GOT2, IL5RA, PYHIN1,ITGA4, HINT1, 200, VAMP2, C15orf54, LGALS2, 199, S100B, HSP90AB1,DNAJC9, PMAIP1, CCR4, RPL17, RUNX2, NPCDR1, JUP, PMS2CL, ANAPC11, PDE3B,RASA4, CAMK1D, LY6G5B, 268, FAM118A, PPIF, B4GALT3, B3GAT3, ABCG1, IRF4,LASS4 (where if a gene name is not provided then a SEQ ID NO. isprovided).

In yet other illustrative examples, a reference ipSIRS IRS biomarkerprofile comprises at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10etc.) IRS biomarker that is upregulated or overexpressed relative to areference healthy condition IRS biomarker profile, illustrative examplesof which include: ATP6V0D1, SAP30, GAB2, KRTAP15-1, NEK6, HDHD1A,SLC39A8, HIST1H2AA, FABP2, CDS2, SRXN1, KLHL5, ACPL2, HS2ST1, HIST1H2BJ,PLIN2, ICAM1, HSPB1, PRR13, P4HA1, SLC11A1, ECHDC3, TAF13, LGALS1,TAAR1, TPX2, DLEU2, TRIM21, AGTRAP, PTGS1, LHFP, CEP97, ACTA2, SIAE,GPR65, IL1RL1, MTHFS, FAM118B, MKI67, LRRFIP1, CCRL2, GALNT2, GSTO1,LRRC70, MTRR, ANKRD28, DPH3, 110, AIG1, UBE2F, LAIR1, PCOLCE2, PLB1,CDA, JKAMP, FRMD3, ITGA2B, SEC24A, RETN, THBS1, MYL9, SPARC, RCBTB2,PLAC8, PDK4, PPP2R5A, SH3PXD2B, DAAM2, NF-E4, DDAH2, MACF1, CD63,CLEC4A, MPO, SUCNR1, EXOSC4, EFCAB2, IL1B, OR9A2, AP3B2, DYNLL1, WSB2,SLC15A2, EAF2, C1orf161, TGFBR1, ABCA1, FFAR2, SLC1A3, ATP13A3, CLU,ADM, IFI16, KCNMA1, C9orf72, GSR, DSE, PICALM, EIF2AK2, HPSE, TLR10,HSPC159, TPST1, ODZ1, STOM, HMGB2, PDGFC, CCR1, OMG, CD163, SGMS2,TREML1, FOXD4L3, C7orf53, CEACAM6, FOLR3, METTL7B, TMEM144, DNAJC13,GALNT3, B3GNT5, CLEC4E, SLC37A3, ABCA13, CD24, C11orf82, FAR2, UBE2J1,GK3P, DEFA4, LTF, ACER3, TMTC1, SMPDL3A, FKBP5, ERLIN1, PLB1, MMP9,KREMEN1, ANKRD34B, OLAH, BMX, PFKFB2, HPGD, BPI, CD274, CEACAM8, TDRD9,LCN2, TNFAIP6, C3AR1, TCN1, IL18R1, CLEC4D, TLR5, RNASE2, FCGR1A, GPR84,OLFM4, VNN1, ARG1, CD177 (where if a gene name is not provided then aSEQ ID NO. is provided).

In yet other illustrative examples, a reference ipSIRS IRS biomarkerprofile comprises: (1) at least one IRS biomarker that is downregulatedor underexpressed relative to a reference healthy condition IRSbiomarker profile, as broadly described above and (2) at least one IRSbiomarker that is upregulated or overexpressed, relative to a referencehealthy condition IRS biomarker profile, as broadly described above.

Yet another subset of the disclosed IRS biomarkers has been identifiedas being useful for assisting in distinguishing between inSIRS affectedsubjects and ipSIRS affected subjects. Accordingly, in some embodiments,the methods and kits are useful for determining the likelihood thatinSIRS or ipSIRS is present or absent in a subject. These methods andkits generally comprise or involve: 1) providing a correlation of areference IRS biomarker profile with the likelihood of having or nothaving inSIRS or ipSIRS, wherein the reference biomarker profileevaluates at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 etc.) IRSbiomarker selected from C11orf82, PLAC8, 132, INSIG1, CDS2, VOPP1,SLC39A9, FOXD4L3, WSB2, CD63, CD274, B3GAT3, CD300A, OCR1, JKAMP, TLR10,PTGER2, PDGFC, LGALS1, HIST1H4L, AGTRAP, AMFR, SIAE, 200, SLC15A2,SLC39A8, TGFBR1, DDAH2, HPSE, SUCNR1, MTRR, GAB2, P4HA1, HS2ST1, MRPL41,TYMS, RUNX2, GSTO1, LRRC70, HIST1H3B, RCBTB2, MPZL3, KIAA1257, AIG1,NEK6, OMG, HIST1H2BM, TDRD9, GALNT3, ATP13A3, C22orf37, SYNE2, ADM,MGST3, PDE3B, HIST1H3I, LOC284757, TRAF3, HIST1H3C, STOM, KLHL5, EXOSC4,C3AR1, KIAA0101, TNFRSF17, HAL, UBE2J1, GLT25D1, CD151, TPX2, PCOLCE2,HSPB1, EAF2, IMP3, PICALM, ACER3, IGL@, HIST1H2BJ, CASS4, ACTA2, PTGS1,KREMEN1, IRS2, TAF13, FSD1L, APOLD1, RBP7, DNAJC13, SEC24A, ERGIC1,FSD1L, TLR5, MKI67, TMEM62, CLEC4A, SDHC, C9orf72, NP, CLU, ABCA1,KIAA0746, PMAIP1, DSE, CMTM5, SMPDL3A, DNAJC9, HDHD1A, HIST1H3H, CDC26,ICAM1, LOC100128751, FAR2, CRIP1, MPZL2, FRMD3, CTSL1, METTL7B, RGS2,CLEC4E, MME, ABCA13, PRR13, HIST1H4C, RRP12, GLDC, ECHDC3, ITGA2B,C7orf53, IRF1, 268, IGK@, RNASE2, FCGR1A, UBE2F, SAP30, LAIR1, PMS2CL,SLC11A1, PLB1, AREG, PPIF, GSR, NFXL1, AP3B2, DCTN5, RPL17, PLA2G7,GALNT2, IGLV6-57, KLRF1, CHI3L1, ANKRD34B, OLFM4, 199, CPM, CCDC125,SULF2, LTF, GPR56, MACF1, PPP1R2, DYNLL1, LCN2, FFAR2, SFRS9, IGJ,FAM118B, 110, ACPL2, HIST1H3A, C7orf58, ANAPC11, HIST1H3J, IRF4, MPO,TREML1, KLRD1, GPR84, CCRL2, CAMK1D, CCR1, ZRANB1, KDM6B, TPST1, HINT1,DAAM2, PTGDR, FKBP5, CD24, HSP90AB1, HPGD, CEACAM8, DEFA4, IL1B, IFI16,CD177, KIAA1324, SRXN1, TAS2R31, CEACAM6, CD163, B4GALT3, ANKRD28,TAAR1, EIF1AX, CYP4F3, 314, HIST1H2AA, LY6G5B, LASS4 (where if a genename is not provided then a SEQ ID NO. is provided); (2) obtaining asample IRS biomarker profile from the subject, which evaluates for anindividual IRS biomarker in the reference IRS biomarker profile acorresponding IRS biomarker; and (3) determining a likelihood of thesubject having or not having inSIRS or ipSIRS based on the sample IRSbiomarker profile and the reference IRS biomarker profile.

In illustrative examples of thus type, a reference inSIRS IRS biomarkerprofile comprises at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10etc.) IRS biomarker that is downregulated or underexpressed relative toa reference ipSIRS IRS biomarker profile, non-limiting examples of whichinclude: OLFM4, CD274, PLACE, LCN2, IGJ, HIST1H4L, HIST1H3C, DEFA4,TDRD9, IGK@, HIST1H3B, CEACAM8, C11orf82, HIST1H2BM, LTF, HPGD, FOXD4L3,PDGFC, CD24, GPR84, CEACAM6, TLR5, SMPDL3A, CD177, HIST1H3I, C3AR1,TLR10, DNAJC9, ABCA13, ANKRD34B, RNASE2, FCGR1A, HPSE, HIST1H3H,KIAA0746, ACER3, SDHC, MTRR, WSB2, CRIP1, IGLV6-57, ATP13A3, CD63,TREML1, PLB1, MRPL41, HIST1H4C, SLC39A8, NP, NFXL1, MPO, ITGA2B, LAIR1,PTGER2, EXOSC4, TYMS, LGALS1, C7orf58, SLC15A2, CD151, ADM, KREMEN1,RCBTB2, PTGS1, AMFR, ABCA1, METTL7B, TNFRSF17, DYNLL1, HSP90AB1, CLU,MKI67, VOPP1, UBE2F, P4HA1, GLT25D1, IL1B, SUCNR1, GALNT3, AIG1, CCR1,OMG, MACF1, CLEC4A, SIAE, FAR2, C7orf53, DNAJC13, HIST1H2BJ, JKAMP,KIAA0101, GSTO1, HSPB1, DDAH2, ICAM1, UBE2J1, KLHL5, HIST1H3J, EAF2,CDS2, MGST3, FFAR2, TPX2, PICALM, HINT1, SLC39A9, SEC24A, STOM, TRAF3,INSIG1, AP3B2, PCOLCE2, B3GAT3, TAF13, CD300A, TGFBR1, HIST1H3A, PMAIP1,AGTRAP, FAM118B, DSE, NEK6, CMTM5, GALNT2, TMEM62, HS2ST1, IGL@, ACTA2,LRRC70, IRF4, GSR, IRF1, EIF1AX, C9orf72, PMS2CL, ANKRD28, CTSL1,C22orf37, FRMD3, HDHD1A, CCRL2, IMP3, RPL17, FSD1L, APOLD1, B4GALT3,FSD1L, DCTN5, PPIF, CDC26, TAS2R31, RRP12, SFRS9, TAAR1, ANAPC11, SRXN1,GLDC, LASS4 (where if a gene name is not provided then a SEQ ID NO. isprovided).

In other illustrative examples, a reference inSIRS IRS biomarker profilecomprises at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 etc.) IRSbiomarker that is upregulated or overexpressed, relative to a referenceipSIRS IRS biomarker profile, representative examples of which include:HIST1H2AA, LY6G5B, 268, IFI16, PPP1R2, CCDC125, ZRANB1, LOC100128751,SLC11A1, GPR56, RUNX2, 110, KDM6B, GAB2, 199, CYP4F3, RGS2, PDE3B,KIAA1257, CAMK1D, CPM, ACPL2, PRR13, ERGIC1, PTGDR, IRS2, MPZL3, MPZL2,AREG, SAP30, RBP7, CASS4, FKBP5, SYNE2, SULF2, KLRD1, 132, KLRF1, 314,LOC284757, HAL, TPST1, ECHDC3, CD163, KIAA1324, PLA2G7, CLEC4E, DAAM2,200, CHI3L1, MME, OCR1 (where if a gene name is not provided then a SEQID NO. is provided).

In still other illustrative examples, an inSIRS IRS biomarker profilecomprises: (1) at least one IRS biomarker that is downregulated orunderexpressed relative to a reference ipSIRS IRS biomarker profile, asbroadly described above and (2) at least one IRS biomarker that isupregulated or overexpressed relative to a reference ipSIRS IRSbiomarker profile, as broadly described above.

Still another subset of the disclosed IRS biomarkers has been identifiedas being useful for assisting in distinguishing between subjects withdifferent stages of ipSIRS selected from mild sepsis, severe sepsis andseptic shock. Accordingly, in some embodiments, the methods and kits areuseful for determining the likelihood that a stage of ipSIRS selectedfrom mild sepsis, severe sepsis and septic shock is present or absent ina subject. These methods and kits generally comprise or involve: 1)providing a correlation of a reference IRS biomarker profile with thelikelihood of having or not having the stage of ipSIRS, wherein thereference biomarker IRS biomarker profile evaluates at least one (e.g.,1, 2, 3, 4, 5, 6, 7, 8, 9, 10 etc.) IRS biomarker selected from PLEKHA3,PLEKHF2, 232, SFRS9, ZNF587, KPNA5, LOC284757, GPR65, VAMP2, SLC1A3,ITK, ATF7, ZNF28, AIF1, MINPP1, GIMAP7, MKI67, IRF4, TSHZ2, HLA-DPB1,EFCAB2, POLE2, FAIM3, 110, CAMK4, TRIM21, IFI44, CENPK, ATP5L, GPR56,HLA-DPA1, C4orf3, GSR, GNLY, RFESD, BPI, HIST1H2AA, NF-E4, CALM2,EIF1AX, E2F6, ARL17P1, TLR5, SH3PXD2B, FAM118A, RETN, PMAIP1, DNAJC9,PCOLCE2, TPX2, BMX, LRRFIP1, DLEU2, JKAMP, JUP, ABCG1, SLC39A9, B3GNT5,ACER3, LRRC70, NPCDR1, TYMS, HLA-DRA, TDRD9, FSD1L, FAR2, C7orf53,PPP1R2, SGMS2, EXOSC4, TGFBR1, CD24, TCN1, TAF13, AP3B2, CD63, SLC15A2,IL18R1, ATP6V0D1, SON, HSP90AB1, CEACAM8, SMPDL3A, IMP3, SEC24A, PICALM,199, CEACAM6, CYP4F3, OLAH, ECHDC3, ODZ1, KIAA0746, KIAA1324, HINT1,VNN1, C22orf37, FSD1L, FOLR3, IL1RL1, OMG, MTHFS, OLFM4, S100B, ITGA4,KLRD1, SLC39A8, KLHL5, KLRK1, MPO, PPIF, GOT2, LRRN3, HIST1H2AJ, CLU,LCN2, 132, CEP97, KLRF1, FBXL13, HIST1H3B, ANKRD34B, RPIA, HPGD,HIST2H2BF, GK3P (where if a gene name is not provided then a SEQ ID NO.is provided).; (2) obtaining a sample IRS biomarker profile from thesubject, which evaluates for an individual IRS biomarker in thereference IRS biomarker profile a corresponding IRS biomarker; and (3)determining a likelihood of the subject having or not having the stageof ipSIRS based on the sample IRS biomarker profile and the referenceIRS biomarker profile.

In illustrative examples of this type, a reference mild sepsis IRSbiomarker profile comprises at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8,9, 10 etc.) IRS biomarker that is downregulated or underexpressedrelative to a reference severe sepsis IRS biomarker profile,illustrative examples of which include: OLFM4, CEACAM8, TCN1, BPI, LCN2,CD24, CEACAM6, NF-E4, HIST1H3B, MKI67, OLAH, TYMS, DNAJC9, MPO,LOC284757, ODZ1, HSP90AB1, VNN1, ANKRD34B, FBXL13, TSHZ2, KIAA0746,FOLR3, GSR, IRF4, LRRN3, TPX2, SFRS9, C7orf53, CYP4F3, IL1RL1, TDRD9,IL18R1, BMX, NPCDR1, GOT2, ATF7, CEP97, ITK, SEC24A, KIAA1324, FAM118A,132, SMPDL3A, CD63, ABCG1, TLR5, CAMK4, CLU, SLC39A9, GK3P, LRRFIP1,AP3B2, SLC15A2, PICALM, HIST1H2AA, SGMS2, OMG, RETN, FAIM3, EXOSC4,SH3PXD2B, FAR2, 199, C4orf3, PCOLCE2 (where if a gene name is notprovided then a SEQ ID NO. is provided).

In other illustrative examples, a reference mild sepsis IRS biomarkerprofile comprises at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10etc.) IRS biomarker that is upregulated or overexpressed, relative to areference severe sepsis IRS biomarker profile, non-limiting examples ofwhich include: JUP, SLC1A3, ECHDC3, IMP3, SLC39A8, MTHFS, TGFBR1, FSD1L,HIST2H2BF, HPGD, FSD1L, PPP1R2, B3GNT5, C22orf37, ACER3, GIMAP7,ATP6V0D1, KLHL5, PPIF, KLRK1, HINT1, GPR56, LRRC70, S100B, 110, SON,ZNF587, JKAMP, ITGA4, HLA-DRA, ZNF28, TRIM21, TAF13, HLA-DPA1, ARL17P1,KLRF1, PMAIP1, RPIA, ATP5L, VAMP2, E2F6, KLRD1, EIF1AX, PLEKHA3, GPR65,CENPK, CALM2, GNLY, DLEU2, HLA-DPB1, AIF1, KPNA5, EFCAB2, PLEKHF2, 232,RFESD, MINPP1, HIST1H2AJ, POLE2, IFI44 (where if a gene name is notprovided then a SEQ ID NO. is provided).

In still other illustrative examples, a reference mild sepsis IRSbiomarker profile comprises: (1) at least one IRS biomarker that isdownregulated or underexpressed relative to a reference severe sepsisIRS biomarker profile, as broadly described above and (2) at least oneIRS biomarker that is upregulated or overexpressed relative to areference severe sepsis IRS biomarker profile, as broadly describedabove.

In other illustrative examples, a reference severe sepsis IRS biomarkerprofile comprises at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10etc.) IRS biomarker that is downregulated or underexpressed relative toa reference septic shock IRS biomarker profile, non-limiting examples ofwhich include: HPGD, SLC1A3, B3GNT5, SMPDL3A, ACER3, RETN, IL18R1,FSD1L, SH3PXD2B, SLC39A8, EXOSC4, FSD1L, AP3B2, ECHDC3, GPR65, TDRD9,BMX, PCOLCE2, PLEKHF2, SGMS2, RPIA, GK3P, FAR2, LRRC70, TGFBR1, MTHFS,C4orf3, TLR5, OLAH, TAF13, JKAMP, POLE2, PICALM, RFESD, ANKRD34B, OMG,VNN1, EIF1AX, KLHL5, SON, LRRFIP1, HIST1H2AJ, AIF1, SLC15A2, CALM2,CD63, HIST1H2AA, MINPP1, S100B, DLEU2, PLEKHA3, ODZ1, FOLR3, 232,EFCAB2, SEC24A, E2F6, SLC39A9, ZNF28, KLRF1, ATP6V0D1, IL1RL1, PPIF(where if a gene name is not provided then a SEQ ID NO. is provided).

In yet other illustrative examples, a reference severe sepsis IRSbiomarker profile comprises at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8,9, 10 etc.) IRS biomarker that is upregulated or overexpressed, relativeto a reference septic shock IRS biomarker profile, representativeexamples of which include: LCN2, CENPK, C22orf37, PMAIP1, KPNA5, ATP5L,TCN1, 132, CD24, ITGA4, KLRD1, SFRS9, TRIM21, VAMP2, GSR, LOC284757,PPP1R2, HINT1, 110, IMP3, C7orf53, ATF7, KIAA0746, GNLY, HLA-DRA, IFI44,ZNF587, CEP97, GPR56, OLFM4, CLU, KLRK1, GOT2, JUP, HLA-DPA1, NPCDR1,TPX2, HIST2H2BF, HLA-DPB1, FAM118A, ABCG1, MKI67, MPO, LRRN3, FBXL13,ARL17P1, CEACAM8, TSHZ2, 199, BPI, HSP90AB1, CYP4F3, TYMS, GIMAP7,DNAJC9, NF-E4, IRF4, HIST1H3B, CAMK4, FAIM3, CEACAM6, ITK, KIAA1324(where if a gene name is not provided then a SEQ ID NO. is provided).

In still other illustrative examples, a reference severe sepsis IRSbiomarker profile comprises: (1) at least one IRS biomarker that isdownregulated or underexpressed relative to a reference septic shock IRSbiomarker profile, as broadly described above and (2) at least one IRSbiomarker that is upregulated or overexpressed relative to a referenceseptic shock IRS biomarker profile, as broadly described above.

In other illustrative examples, a reference septic shock IRS biomarkerprofile comprises at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10etc.) IRS biomarker that is downregulated or underexpressed relative toa reference mild sepsis IRS biomarker profile, representative examplesof which include: IFI44, HLA-DPB1, ARL17P1, HIST1H2AJ, MINPP1, GNLY,GIMAP7, HLA-DPA1, POLE2, 232, KPNA5, GPR56, HLA-DRA, ZNF587, KLRK1,RFESD, VAMP2, CENPK, KIAA1324, KLRD1, EFCAB2, ATP5L, 110, ITK, FAIM3,TRIM21, PMAIP1, HIST2H2BF, HINT1, DLEU2, AIF1, E2F6, ITGA4, KLRF1,CALM2, PLEKHA3, PPP1R2, CAMK4, 199, ZNF28, PLEKHF2, JUP, EIF1AX, PPIF,IMP3, C22orf37, ATP6V0D1, S100B, SON, GPR65, ABCG1, TAF13, FAM118A,RPIA, KLHL5, JKAMP, IRF4, CLU, CYP4F3, LRRC70 (where if a gene name isnot provided then a SEQ ID NO. is provided).

In yet other illustrative examples, a reference septic shock IRSbiomarker profile comprises at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8,9, 10 etc.) IRS biomarker that is upregulated or overexpressed, relativeto a reference mild sepsis IRS biomarker profile, illustrative examplesof which include: GOT2, NPCDR1, CEP97, LRRN3, DNAJC9, TSHZ2, HSP90AB1,TYMS, HIST1H3B, ATF7, FBXL13, TPX2, TGFBR1, MPO, 132, NF-E4, MTHFS,CEACAM6, C7orf53, FSD1L, FSD1L, SLC39A9, MKI67, KIAA0746, HIST1H2AA,ACER3, ECHDC3, SLC15A2, SLC39A8, SEC24A, SFRS9, LRRFIP1, OMG, GSR,C4orf3, CD63, PICALM, LOC284757, FAR2, PCOLCE2, IL1RL1, B3GNT5, SGMS2,TLR5, EXOSC4, SH3PXD2B, GK3P, AP3B2, FOLR3, BPI, RETN, ODZ1, CEACAM8,BMX, HPGD, VNN1, ANKRD34B, SLC1A3, TDRD9, SMPDL3A, CD24, IL18R1, OLAH,LCN2, TCN1, OLFM4 (where if a gene name is not provided then a SEQ IDNO. is provided).

In yet other illustrative examples, a reference septic shock IRSbiomarker profile comprises: (1) at least one IRS biomarker that isdownregulated or underexpressed relative to a reference mild sepsis IRSbiomarker profile, as broadly described above and (2) at least one IRSbiomarker that is upregulated or overexpressed relative to a referencemild sepsis IRS biomarker profile, as broadly described above.

In some embodiments, individual IRS biomarkers as broadly describedabove and elsewhere herein are selected from the group consisting of:(a) a polynucleotide expression product comprising a nucleotide sequencethat shares at least 70% (or at least 71% to at least 99% and allinteger percentages in between) sequence identity with the sequence setforth in any one of SEQ ID NO: 1-319, or a complement thereof; (b) apolynucleotide expression product comprising a nucleotide sequence thatencodes a polypeptide comprising the amino acid sequence set forth inany one of SEQ ID NO: 320-619; (c) a polynucleotide expression productcomprising a nucleotide sequence that encodes a polypeptide that sharesat least 70% (or at least 71% to at least 99% and all integerpercentages in between) sequence similarity or identity with at least aportion of the sequence set forth in SEQ ID NO: 320-619; (d) apolynucleotide expression product comprising a nucleotide sequence thathybridizes to the sequence of (a), (b), (c) or a complement thereof,under medium or high stringency conditions; (e) a polypeptide expressionproduct comprising the amino acid sequence set forth in any one of SEQID NO: 320-619; and (f) a polypeptide expression product comprising anamino acid sequence that shares at least 70% (or at least 71% to atleast 99% and all integer percentages in between) sequence similarity oridentity with the sequence set forth in any one of SEQ ID NO: 320-619.

In some embodiments, the methods and kits comprise or involve: (1)measuring in the biological sample the level of an expression product ofat least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more) IRS biomarkergene and (2) comparing the measured level or functional activity of eachexpression product to the level or functional activity of acorresponding expression product in a reference sample.

The present invention also extends to the management of inSIRS, ipSIRSor particular stages of ipSIRS, or prevention of further progression ofinSIRS, ipSIRS or particular stages of ipSIRS (e.g., mild sepsis, severesepsis and septic shock), or assessment of the efficacy of therapies insubjects following positive diagnosis for the presence of inSIRS, ipSIRSor particular stage of ipSIRS (e.g., mild sepsis, severe sepsis andseptic shock) in a subject. The management of inSIRS or ipSIRSconditions is generally highly intensive and can include identificationand amelioration of the underlying cause and aggressive use oftherapeutic compounds such as, vasoactive compounds, antibiotics,steroids, antibodies to endotoxin, anti tumour necrosis factor agents,recombinant protein C. In addition, palliative therapies as describedfor example in Cohen and Glauser (1991, Lancet 338: 736-739) aimed atrestoring and protecting organ function can be used such as intravenousfluids and oxygen and tight glycemic control. Therapies for ipSIRS arereviewed in Healy (2002, Ann. Pharmacother. 36(4): 648-54) and Brindley(2005, CJEM. 7(4): 227) and Jenkins (2006, J Hosp Med. 1(5): 285-295).

Typically, the therapeutic agents will be administered in pharmaceutical(or veterinary) compositions together with a pharmaceutically acceptablecarrier and in an effective amount to achieve their intended purpose.The dose of active compounds administered to a subject should besufficient to achieve a beneficial response in the subject over timesuch as a reduction in, or relief from, the symptoms of inSIRS, ipSIRSor particular stages of ipSIRS. The quantity of the pharmaceuticallyactive compounds(s) to be administered may depend on the subject to betreated inclusive of the age, sex, weight and general health conditionthereof. In this regard, precise amounts of the active compound(s) foradministration will depend on the judgment of the practitioner. Indetermining the effective amount of the active compound(s) to beadministered in the treatment or prevention of inSIRS, ipSIRS orparticular stages of ipSIRS, the medical practitioner or veterinarianmay evaluate severity of any symptom associated with the presence ofinSIRS, ipSIRS or particular stages of ipSIRS including, inflammation,blood pressure anomaly, tachycardia, tachypnea fever, chills, vomiting,diarrhoea, skin rash, headaches, confusion, muscle aches, seizures. Inany event, those of skill in the art may readily determine suitabledosages of the therapeutic agents and suitable treatment regimenswithout undue experimentation.

The therapeutic agents may be administered in concert with adjunctive(palliative) therapies to increase oxygen supply to major organs,increase blood flow to major organs and/or to reduce the inflammatoryresponse. Illustrative examples of such adjunctive therapies include nonsteroidal-anti inflammatory drugs (NSAIDs), intravenous saline andoxygen.

Thus, the present invention contemplates the use of the methods and kitsdescribed above and elsewhere herein in methods for treating, preventingor inhibiting the development of inSIRS, ipSIRS or a particular stage ofipSIRS (e.g., mild sepsis, severe sepsis and septic shock) in a subject.These methods generally comprise (1) correlating a reference IRSbiomarker profile with the presence or absence of a condition selectedfrom a healthy condition, SIRS, inSIRS, ipSIRS, or a particular stage ofipSIRS, wherein the reference IRS biomarker profile evaluates at leastone (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 etc.) IRS biomarker; (2)obtaining an IRS biomarker profile of a sample (i.e., “a sample IRSbiomarker profile”) from a subject, wherein the sample IRS biomarkerprofile evaluates for an individual IRS biomarker in the reference IRSbiomarker profile a corresponding IRS biomarker; (3) determining alikelihood of the subject having or not having the condition based onthe sample IRS biomarker profile and the reference IRS biomarkerprofile, and administering to the subject, on the basis that the subjecthas an increased likelihood of having inSIRS, an effective amount of anagent that treats or ameliorates the symptoms or reverses or inhibitsthe development of inSIRS, or administering to the subject, on the basisthat the subject has an increased likelihood of having ipSIRS or aparticular stage of ipSIRS, an effective amount of an agent that treatsor ameliorates the symptoms or reverses or inhibits the development ofipSIRS or the particular stage of ipSIRS.

In some embodiments the methods and kits of the present invention areused for monitoring, treatment and management of conditions that canlead to inSIRS or ipSIRS, illustrative examples of which includeretained placenta, meningitis, endometriosis, shock, toxic shock (i.e.,sequelae to tampon use), gastroenteritis, appendicitis, ulcerativecolitis, Crohn's disease, inflammatory bowel disease, acid gut syndrome,liver failure and cirrhosis, failure of colostrum transfer in neonates,ischemia (in any organ), bacteraemia, infections within body cavitiessuch as the peritoneal, pericardial, thecal, and pleural cavities,burns, severe wounds, excessive exercise or stress, haemodialysis,conditions involving intolerable pain (e.g., pancreatitis, kidneystones), surgical operations, and non-healing lesions. In theseembodiments, the methods or kits of the present invention are typicallyused at a frequency that is effective to monitor the early developmentof inSIRS, ipSIRS or particular stages of ipSIRS, to thereby enableearly therapeutic intervention and treatment of that condition. Inillustrative examples, the diagnostic methods or kits are used at leastat 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, 21, 22, 23 or 24 hour intervals or at least 1, 2, 3, 4, 5 or 6 dayintervals, or at least weekly, fortnightly or monthly. Accordingly, thepresent invention encompasses the use of the methods and kits of thepresent invention for early diagnosis of inSIRS, ipSIRS or particularstages of ipSIRS.

The term “early diagnosis” as used herein with “early detection” refersto specific screening/monitoring processes that allow detection andevaluation of inSIRS, ipSIRS or particular stages of ipSIRS at an earlypoint in disease development and/or progression. For example, since bothinSIRS and ipSIRS patients present with similar clinical signs, earlydetection of ipSIRS can be achieved through a plurality of evaluationsof patients with inSIRS to detect a transition to ipSIRS.

The present invention can be practiced in the field of predictivemedicine for the purposes of diagnosis or monitoring the presence ordevelopment of a condition selected from inSIRS, ipSIRS or a particularstage of ipSIRS in a subject, and/or monitoring response to therapyefficacy.

The IRS biomarker profile further enables determination of endpoints inpharmacotranslational studies. For example, clinical trials can takemany months or even years to establish the pharmacological parametersfor a medicament to be used in treating or preventing inSIRS, ipSIRS ora particular stage of ipSIRS (e.g., mild sepsis, severe sepsis andseptic shock). However, these parameters may be associated with an IRSbiomarker profile associated with a health state (e.g., a healthycondition). Hence, the clinical trial can be expedited by selecting atreatment regimen (e.g., medicament and pharmaceutical parameters),which results in an IRS biomarker profile associated with the desiredhealth state (e.g., healthy condition). This may be determined forexample by (1) providing a correlation of a reference IRS biomarkerprofile with the likelihood of having the healthy condition; (2)obtaining a corresponding IRS biomarker profile of a subject havinginSIRS, ipSIRS or a particular stage of ipSIRS after treatment with atreatment regimen, wherein a similarity of the subject's IRS biomarkerprofile after treatment to the reference IRS biomarker profile indicatesthe likelihood that the treatment regimen is effective for changing thehealth status of the subject to the desired health state (e.g., healthycondition). This aspect of the present invention advantageously providesmethods of monitoring the efficacy of a particular treatment regimen ina subject (for example, in the context of a clinical trial) alreadydiagnosed with a condition selected from inSIRS, ipSIRS or a particularstage of ipSIRS. These methods take advantage of IRS biomarkers thatcorrelate with treatment efficacy, for example, to determine whether theIRS biomarker profile of a subject undergoing treatment partially orcompletely normalizes during the course of or following therapy orotherwise shows changes associated with responsiveness to the therapy.

The IRS biomarker profile further enables stratification of patientsprior to enrolment in pharmacotranslational studies. For example, aclinical trial can be expedited by selecting a priori patients with aparticular IRS biomarker profile that would most benefit from aparticular treatment regimen (e.g., medicament and pharmaceuticalparameters). For instance, patient enrolment into a clinical trialtesting the efficacy of a new antibiotic would best include patientswith an IRS biomarker profile that indicated that they had ipSIRS ratherthan inSIRS, and as such the selected patients would most likely benefitfrom the new therapy. Further, and by example, patient enrolment into aclinical trial testing the efficacy of a new inotrope would best includepatients with an IRS biomarker profile that indicated that they had theshock stage of ipSIRS rather than inSIRS or other stage of ipSIRS, andas such the selected patients would most likely benefit from the newtherapy.

As used herein, the term “treatment regimen” refers to prophylacticand/or therapeutic (i.e., after onset of a specified condition)treatments, unless the context specifically indicates otherwise. Theterm “treatment regimen” encompasses natural substances andpharmaceutical agents (i.e., “drugs”) as well as any other treatmentregimen including but not limited to dietary treatments, physicaltherapy or exercise regimens, surgical interventions, and combinationsthereof.

Thus, the invention provides methods of correlating a reference IRSbiomarker profile with an effective treatment regimen for a conditionselected from inSIRS, ipSIRS or a particular stage of ipSIRS (e.g., mildsepsis, severe sepsis and septic shock), wherein the reference IRSbiomarker profile evaluates at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8,9, 10, etc.) IRS biomarker. These methods generally comprise: (a)determining a sample IRS biomarker profile from a subject with thecondition prior to treatment (i.e., baseline), wherein the sample IRSbiomarker profile evaluates for an individual IRS biomarker in thereference IRS biomarker profile a corresponding IRS biomarker; andcorrelating the sample IRS biomarker profile with a treatment regimenthat is effective for treating that condition.

The invention further provides methods of determining whether atreatment regimen is effective for treating a subject with a conditionselected from inSIRS, ipSIRS or a particular stage of ipSIRS (e.g., mildsepsis, severe sepsis and septic shock). These methods generallycomprise: (a) correlating a reference biomarker profile prior totreatment (i.e., baseline) with an effective treatment regimen for thecondition, wherein the reference IRS biomarker profile evaluates atleast one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, etc.) IRS biomarker; and(b) obtaining a sample IRS biomarker profile from the subject aftertreatment, wherein the sample IRS biomarker profile evaluates for anindividual IRS biomarker in the reference IRS biomarker profile acorresponding IRS biomarker, and wherein the sample IRS biomarkerprofile after treatment indicates whether the treatment regimen iseffective for treating the condition in the subject.

The invention can also be practiced to evaluate whether a subject isresponding (i.e., a positive response) or not responding (i.e., anegative response) to a treatment regimen. This aspect of the inventionprovides methods of correlating an IRS biomarker profile with a positiveand/or negative response to a treatment regimen. These methods generallycomprise: (a) obtaining an IRS biomarker profile from a subject with acondition selected from inSIRS, ipSIRS or a particular stage of ipSIRS(e.g., mild sepsis, severe sepsis and septic shock) followingcommencement of the treatment regimen, wherein the IRS biomarker profileevaluates at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, etc.) IRSbiomarker; and (b) correlating the IRS biomarker profile from thesubject with a positive and/or negative response to the treatmentregimen.

The invention also provides methods of determining a positive and/ornegative response to a treatment regimen by a subject with a conditionselected from inSIRS, ipSIRS or a particular stage of ipSIRS (e.g., mildsepsis, severe sepsis and septic shock). These methods generallycomprise: (a) correlating a reference IRS biomarker profile with apositive and/or negative response to the treatment regimen, wherein thereference IRS biomarker profile evaluates at least one (e.g., 1, 2, 3,4, 5, 6, 7, 8, 9, 10, etc.) IRS biomarker; and (b) determining a sampleIRS biomarker profile from the subject, wherein the subject's sample IRSbiomarker profile evaluates for an individual IRS biomarker in thereference IRS biomarker profile a corresponding IRS biomarker andindicates whether the subject is responding to the treatment regimen.

In some embodiments, the methods further comprise determining a firstsample IRS biomarker profile from the subject prior to commencing thetreatment regimen (i.e., a baseline profile), wherein the first sampleIRS biomarker profile evaluates at least one (e.g., 1, 2, 3, 4, 5, 6, 7,8, 9, 10, etc.) IRS biomarker; and comparing the first sample IRSbiomarker profile with a second sample IRS biomarker profile from thesubject after commencement of the treatment regimen, wherein the secondsample IRS biomarker profile evaluates for an individual IRS biomarkerin the first sample IRS biomarker profile a corresponding IRS biomarker.

This aspect of the invention can be practiced to identify responders ornon-responders relatively early in the treatment process, i.e., beforeclinical manifestations of efficacy. In this way, the treatment regimencan optionally be discontinued, a different treatment protocol can beimplemented and/or supplemental therapy can be administered. Thus, insome embodiments, a sample IRS biomarker profile is obtained withinabout 2 hours, 4 hours, 6 hours, 12 hours, 1 day, 2 days, 3 days, 4days, 5 days, 1 week, 2 weeks, 3 weeks, 4 weeks, 6 weeks, 8 weeks, 10weeks, 12 weeks, 4 months, six months or longer of commencing therapy.

In order that the invention may be readily understood and put intopractical effect, particular preferred embodiments will now be describedby way of the following non-limiting examples.

EXAMPLES Example 1 Monitoring Severity of ipSIRS in Patients inIntensive Care

Patients admitted to intensive care (ICU) often have ipSIRS, or developipSIRS during their ICU stay. The ultimate aim of intensive care is toensure the patient survives and is discharged to a general ward in theminimum time. Patients in intensive care with diagnosed ipSIRS areusually administered a number of therapeutic compounds—many of whichhave opposing actions on the immune system and many of which could becounterproductive depending on the severity of ipSIRS (mild sepsis,severe sepsis, septic shock). Monitoring intensive care patients on aregular basis with biomarkers of the present invention will allowmedical practitioners to determine the stage of ipSIRS and hence choiceof therapies and patient management procedures, and ultimately responseto therapy. Information provided by these biomarkers disclosed herein(“the IRS biomarkers”) will therefore allow medical practitioners totailor and modify therapies to ensure patients survive and spend lesstime in intensive care. Less time in intensive care leads toconsiderable savings in medical expenses including through lessoccupancy time and appropriate use and timing of medications. Practicalexamples of the use of the IRS biomarkers in Tables 1-6 are described.

Tables 1, 2 and 3 list those top 10 IRS biomarkers (by example) inascending order of p value (less than 0.05) when comparing the clinicalgroups of mild sepsis, severe sepsis and septic shock (severe versusmild, shock versus mild and shock versus severe—the appropriate columnis filled grey for each group in respective tables). In this and thefollowing examples significance is defined when a p value is less than0.05. P values were determined by adjusted t-test (Benjamini & Hochberg,1995) in the case of healthy vs. other and inSIRS vs. ipSIRS, and byTukey's Honestly Significant Difference for analysis of variance (ANOVA)for the mild/severe/shock comparisons. For the groups severe versusmild, shock versus mild and shock versus severe there were 72, 120 and47 biomarkers respectively with a p value less than 0.05.

Tables 4, 5 and 6 list those top 10 biomarkers (by example) indescending order of Area Under Curve (AUC) value when comparing theclinical groups of mild sepsis, severe sepsis and septic shock (severeversus mild, shock versus mild and shock versus severe—the appropriatecolumn is filled grey for each group in respective tables). For thegroups severe versus mild, shock versus mild and shock versus severethere were 34, 17 and 2 biomarkers respectively with an AUC greater than0.8 (a nominal cut-off above which would be considered to be good).

In each of Tables 1-6 a SEQ ID NO. is provided for each IRS biomarker(IRS biomarker polynucleotides range from SEQ ID NO. 1-319, IRSbiomarker polypeptides range from SEQ ID No. 320-619), along with adatabase identification tag (e.g. NM_), a gene name (Gene Name) if thereis one, and either; mean expression values for healthy (HC), inSIRS,mild sepsis, severe sepsis and septic shock, and p values for HC vs. allother groups, inSIRS vs. ipSIRS, mild sepsis versus severe sepsis, mildsepsis versus septic shock and septic shock versus severe sepsis, or AUCvalues for HC vs. Sick, HC vs. inSIRS, HC vs. ipSIRS, inSIRS vs. ipSIRS,Mild Sepsis versus Severe Sepsis, Mild Sepsis versus Septic Shock andSeptic Shock versus Severe Sepsis. Such biomarkers have clinical utilityin determining ipSIRS severity based on these groups. By example, inTable 1, Severe versus Mild p Value, it can be seen that the genePLEKHA3 has a significant p value for both Severe versus Mild and Shockversus Mild and therefore has utility in separating mild sepsis fromboth severe sepsis and septic shock. In Table 2, Severe versus Mild AreaUnder Curve, it can be seen that the gene PLEKHA3 has an AUC of 0.8748and therefore has most utility in separating mild sepsis from severesepsis. It can be seen that the p value for PLEKHA3 for separatingseptic shock from severe sepsis is not significant (>0.05) and thereforethis biomarker has no utility in separating these two groups. From thecolumns in the table containing mean expression data it can be seen thatPLEKHA3 is down-regulated in both severe sepsis (6.689) and septic shock(6.825) compared to mild sepsis (7.281) (also see FIG. 1).

Further and by example in Table 3, Shock vs. Mild p Value, it can beseen that the biomarker VAMP2 has utility in separating mild sepsis fromseptic shock but also from severe sepsis. VAMP2 does not have utility inseparating septic shock from severe sepsis (p=0.708038) but does havefurther utility in separating healthy from other groups. From the meanexpression columns it can also be seen that the expression level ofVAMP2 is downregulated in both severe sepsis (8.454) and septic shock(8.353) compared to mild sepsis (9.016) (see also FIG. 2). In Table 4,Shock vs. Mild Area Under Curve, it can be seen that VAMP2 has an AUC of0.8342.

Further and by example in Table 5, Shock versus Severe p Value, it canbe seen that the biomarker ITK has utility in separating Shock versusSevere Sepsis and Mild Sepsis, and healthy from other groups but noutility in separating Severe Sepsis and Mild Sepsis. From the meanexpression values for ITK it can be seen that it is comparativelydownregulated in Septic Shock compared to both Severe and Mild Sepsis(see also FIG. 3). In Table 6, Shock versus Severe Area Under Curve, itcan be seen that ITK has an AUC of 0.8054.

TABLE 1 Severe versus Mild p Value pval pval pval SEQ ID Database HCinSIRS Mild Severe Shock pval.HC. vs. pval.inSIRS. Severe. Shock. vs.Shock. vs. Number ID Gene Name Mean Mean Mean Mean Mean Other vs. ipSIRSvs. Mild Mild Severe 285 NM_019091 PLEKHA3 7.058 6.910 7.281 6.689 6.8251.000000 1.000000 0.000001 0.000011 0.409707 587 NM_019091 PLEKHA3 7.0586.910 7.281 6.689 6.825 1.000000 1.000000 0.000001 0.000011 0.409707 232gi|14250459 NA 6.389 5.970 6.875 5.976 6.059 1.000000 0.638753 0.0000010.000000 0.869569 gi|14250459 NA 6.389 5.970 6.875 5.976 6.059 1.0000000.638753 0.000001 0.000000 0.869569 195 NM_004897 MINPP1 8.212 7.6978.228 7.151 7.317 0.006705 1.000000 0.000005 0.000008 0.707929 504NM_004897 MINPP1 8.212 7.697 8.228 7.151 7.317 0.006705 1.0000000.000005 0.000008 0.707929 288 NM_024613 PLEKHF2 7.432 7.660 8.044 7.2557.671 0.789194 1.000000 0.000012 0.023331 0.029137 590 NM_024613 PLEKHF27.432 7.660 8.044 7.255 7.671 0.789194 1.000000 0.000012 0.0233310.029137 190 NM_176818 SFRS9 9.739 9.666 9.715 10.226 10.128 1.0000000.000618 0.000018 0.000064 0.627141 499 NM_176818 SFRS9 9.739 9.6669.715 10.226 10.128 1.000000 0.000618 0.000018 0.000064 0.627141 207NR_002612 DLEU2 6.549 6.894 7.347 6.699 6.850 0.000171 1.000000 0.0000190.000152 0.504529 NR_002612 DLEU2 6.549 6.894 7.347 6.699 6.850 0.0001711.000000 0.000019 0.000152 0.504529 197 NM_006969 ZNF28 4.936 4.5115.008 4.554 4.612 0.021118 0.027741 0.000020 0.000018 0.814925 506NM_006969 ZNF28 4.936 4.511 5.008 4.554 4.612 0.021118 0.027741 0.0000200.000018 0.814925 71 NM_002269 KPNA5 6.822 5.908 6.451 5.758 5.7120.000000 1.000000 0.000021 0.000000 0.945115 387 NM_002269 KPNA5 6.8225.908 6.451 5.758 5.712 0.000000 1.000000 0.000021 0.000000 0.945115 278NM_001130059 ATF7 5.212 5.212 5.253 5.605 5.372 1.000000 0.4380130.000033 0.171523 0.007397 580 NM_001130059 ATF7 5.212 5.212 5.253 5.6055.372 1.000000 0.438013 0.000033 0.171523 0.007397 81 NR_046099LOC284757 6.913 8.028 6.894 7.534 7.376 0.000001 0.000004 0.0000390.000364 0.493398 NR_046099 LOC284757 6.913 8.028 6.894 7.534 7.3760.000001 0.000004 0.000039 0.000364 0.493398

TABLE 2 Severe versus Mild Area Under Curve (AUC) SEQ ID HC vs. Sick HCvs. SIRS HC vs. inSIRS vs. Mild vs. Mild vs. Severe vs. Number DatabaseID Gene Name AUC AUC ipSIRS AUC ipSIRS AUC Severe AUC Shock AUC ShockAUC 195 NM_004897 MINPP1 0.689217443 0.699775533 0.685388685 0.5321345320.88707483 0.779591837 0.488435374 504 NM_004897 MINPP1 0.6892174430.699775533 0.685388685 0.532134532 0.88707483 0.779591837 0.488435374285 NM_019091 PLEKHA3 0.584378734 0.644781145 0.562474562 0.5417915420.874829932 0.794285714 0.619047619 587 NM_019091 PLEKHA3 0.5843787340.644781145 0.562474562 0.541791542 0.874829932 0.794285714 0.619047619282 NM_002692 POLE2 0.675029869 0.693602694 0.668294668 0.5011655010.854421769 0.707755102 0.563265306 584 NM_002692 POLE2 0.6750298690.693602694 0.668294668 0.501165501 0.854421769 0.707755102 0.563265306288 NM_024613 PLEKHF2 0.654868578 0.667227834 0.65038665 0.5387945390.850340136 0.692244898 0.718367347 590 NM_024613 PLEKHF2 0.6548685780.667227834 0.65038665 0.538794539 0.850340136 0.692244898 0.718367347190 NM_176818 SFRS9 0.597520908 0.557800224 0.653846154 0.7489177490.840816327 0.79755102 0.587755102 499 NM_176818 SFRS9 0.5975209080.557800224 0.653846154 0.748917749 0.840816327 0.79755102 0.587755102170 NM_003608 GPR65 0.765681004 0.735690236 0.776556777 0.60039960.839455782 0.615510204 0.740136054 480 NM_003608 GPR65 0.7656810040.735690236 0.776556777 0.6003996 0.839455782 0.615510204 0.740136054232 gi|14250459 NA 0.571983274 0.7003367 0.525437525 0.6613386610.838095238 0.83755102 0.559183673 gi|14250459 NA 0.571983274 0.70033670.525437525 0.661338661 0.838095238 0.83755102 0.559183673 197 NM_006969ZNF28 0.689964158 0.837261504 0.636548637 0.670662671 0.8367346940.796734694 0.536054422 506 NM_006969 ZNF28 0.689964158 0.8372615040.636548637 0.670662671 0.836734694 0.796734694 0.536054422 81 NR_046099LOC284757 0.738948626 0.939393939 0.666259666 0.835497835 0.8340136050.749387755 0.613605442 NR_046099 LOC284757 0.738948626 0.9393939390.666259666 0.835497835 0.834013605 0.749387755 0.613605442 71 NM_002269KPNA5 0.85483871 0.895061728 0.84025234 0.564768565 0.8312925170.844897959 0.530612245 387 NM_002269 KPNA5 0.85483871 0.8950617280.84025234 0.564768565 0.831292517 0.844897959 0.530612245

TABLE 3 Shock vs. Mild p Value SEQ ID pval pval pval Num- Gene HC inSIRSMild Severe Shock pval.HC. vs. pval.inSIRS. Severe. vs. Shock. vs.Shock. ber Database ID Name Mean Mean Mean Mean Mean Other vs. IpSIRSMild Mild vs. Severe 59 NM_014232 VAMP2 9.213 8.896 9.016 8.454 8.3530.000000 0.297018 0.000084 0.000000 0.708038 376 NM_014232 VAMP2 9.2138.896 9.016 8.454 8.353 0.000000 0.297018 0.000084 0.000000 0.708038 71NM_002269 KPNA5 6.822 5.908 6.451 5.758 5.712 0.000000 1.000000 0.0000210.000000 0.945115 387 NM_002269 KPNA5 6.822 5.908 6.451 5.758 5.7120.000000 1.000000 0.000021 0.000000 0.945115 232 gi|14250459 NA 6.3895.970 6.875 5.976 6.059 1.000000 0.638753 0.000001 0.000000 0.869569gi|14250459 NA 6.389 5.970 6.875 5.976 6.059 1.000000 0.638753 0.0000010.000000 0.869569 246 NM_032828 ZNF587 8.514 8.816 8.783 8.381 8.1011.000000 0.003242 0.018153 0.000001 0.136854 549 NM_032828 ZNF587 8.5148.816 8.783 8.381 8.101 1.000000 0.003242 0.018153 0.000001 0.136854 195NM_004897 MINPP1 8.212 7.697 8.228 7.151 7.317 0.006705 1.0000000.000005 0.000008 0.707929 504 NM_004897 MINPP1 8.212 7.697 8.228 7.1517.317 0.006705 1.000000 0.000005 0.000008 0.707929 107 NM_153236 GIMAP79.533 8.865 8.974 8.682 8.112 0.000000 1.000000 0.310769 0.0000080.014285 420 NM_153236 GIMAP7 9.533 8.865 8.974 8.682 8.112 0.0000001.000000 0.310769 0.000008 0.014285 285 NM_019091 PLEKHA3 7.058 6.9107.281 6.689 6.825 1.000000 1.000000 0.000001 0.000011 0.409707 587NM_019091 PLEKHA3 7.058 6.910 7.281 6.689 6.825 1.000000 1.0000000.000001 0.000011 0.409707 58 NM_002121 HLA-DPB1 11.414 10.665 10.6239.971 9.578 0.000000 0.067333 0.026428 0.000013 0.253098 375 NM_002121HLA-DPB1 11.414 10.665 10.623 9.971 9.578 0.000000 0.067333 0.0264280.000013 0.253098 304 NR_033759 ATP5L 7.242 7.337 7.379 6.824 6.7721.000000 0.748516 0.000612 0.000014 0.929559 NR_033759 ATP5L 7.242 7.3377.379 6.824 6.772 1.000000 0.748516 0.000612 0.000014 0.929559 197NM_006969 ZNF28 4.936 4.511 5.008 4.554 4.612 0.021118 0.027741 0.0000200.000018 0.814925 506 NM_006969 ZNF28 4.936 4.511 5.008 4.554 4.6120.021118 0.027741 0.000020 0.000018 0.814925

TABLE 4 Shock vs. Mild Area Under Curve (AUC) SEQ ID HC vs. Sick HC vs.SIRS HC vs. inSIRS vs. Mild vs. Mild vs. Severe vs. Number Database IDGene Name AUC AUC ipSIRS AUC ipSIRS AUC Severe AUC Shock AUC Shock AUC71 NM_002269 KPNA5 0.85483871 0.895061728 0.84025234 0.5647685650.831292517 0.844897959 0.530612245 387 NM_002269 KPNA5 0.854838710.895061728 0.84025234 0.564768565 0.831292517 0.844897959 0.530612245246 NM_032828 ZNF587 0.506571087 0.632435466 0.539072039 0.689643690.723809524 0.84244898 0.644897959 549 NM_032828 ZNF587 0.5065710870.632435466 0.539072039 0.68964369 0.723809524 0.84244898 0.644897959232 gi|14250459 NA 0.571983274 0.7003367 0.525437525 0.6613386610.838095238 0.83755102 0.559183673 gi|14250459 NA 0.571983274 0.70033670.525437525 0.661338661 0.838095238 0.83755102 0.559183673 59 NM_014232VAMP2 0.814366786 0.78338945 0.825600326 0.636363636 0.8108843540.834285714 0.54829932 376 NM_014232 VAMP2 0.814366786 0.783389450.825600326 0.636363636 0.810884354 0.834285714 0.54829932 107 NM_153236GIMAP7 0.829898447 0.80359147 0.839438339 0.603063603 0.6176870750.826938776 0.710204082 420 NM_153236 GIMAP7 0.829898447 0.803591470.839438339 0.603063603 0.617687075 0.826938776 0.710204082 58 NM_002121HLA-DPB1 0.857228196 0.814814815 0.872608873 0.682317682 0.6938775510.809795918 0.628571429 375 NM_002121 HLA-DPB1 0.857228196 0.8148148150.872608873 0.682317682 0.693877551 0.809795918 0.628571429 110gi|13182974 NA 0.796893668 0.92704826 0.74969475 0.743589744 0.6816326530.808163265 0.617687075 423 gi|13182974 NA 0.796893668 0.927048260.74969475 0.743589744 0.681632653 0.808163265 0.617687075 64 NM_004172SLC1A3 0.954599761 0.985409652 0.943426943 0.636030636 0.5578231290.806530612 0.823129252 381 NM_004172 SLC1A3 0.954599761 0.9854096520.943426943 0.636030636 0.557823129 0.806530612 0.823129252 190NM_176818 SFRS9 0.597520908 0.557800224 0.653846154 0.7489177490.840816327 0.79755102 0.587755102 499 NM_176818 SFRS9 0.5975209080.557800224 0.653846154 0.748917749 0.840816327 0.79755102 0.587755102197 NM_006969 ZNF28 0.689964158 0.837261504 0.636548637 0.6706626710.836734694 0.796734694 0.536054422 506 NM_006969 ZNF28 0.6899641580.837261504 0.636548637 0.670662671 0.836734694 0.796734694 0.536054422

TABLE 5 Shock versus Severe p Value SEQ ID pval pval pval Num- Gene HCinSIRS Mild Severe Shock pval.HC. vs. pval.inSIRS. vs. Severe. vs.Shock. vs. Shock. vs. ber Database ID Name Mean Mean Mean Mean MeanOther Sepsis Mild Mild Severe 79 NM_005546 ITK 9.271 8.099 8.227 8.5367.635 0.000000 1.000000 0.276607 0.002832 0.000063 395 NM_005546 ITK9.271 8.099 8.227 8.536 7.635 0.000000 1.000000 0.276607 0.0028320.000063 34 NM_001744 CAMK4 8.155 6.723 6.902 7.152 6.470 0.0000001.000000 0.305982 0.011176 0.000339 352 NM_001744 CAMK4 8.155 6.7236.902 7.152 6.470 0.000000 1.000000 0.305982 0.011176 0.000339 64NM_004172 SLC1A3 5.849 6.892 6.472 6.447 7.373 0.000000 1.0000000.993673 0.000056 0.000352 381 NM_004172 SLC1A3 5.849 6.892 6.472 6.4477.373 0.000000 1.000000 0.993673 0.000056 0.000352 171 NR_046000 IRF48.200 7.491 7.843 8.381 7.762 0.000105 0.000070 0.002770 0.8255560.000501 NR_046000 IRF4 8.200 7.491 7.843 8.381 7.762 0.000105 0.0000700.002770 0.825556 0.000501 271 NM_173485 TSHZ2 7.382 6.972 6.846 7.4296.929 0.004682 1.000000 0.000051 0.739871 0.000534 574 NM_173485 TSHZ27.382 6.972 6.846 7.429 6.929 0.004682 1.000000 0.000051 0.7398710.000534 22 NM_005449 FAIM3 10.259 9.101 9.036 9.174 8.464 0.0000001.000000 0.732360 0.001453 0.000582 340 NM_005449 FAIM3 10.259 9.1019.036 9.174 8.464 0.000000 1.000000 0.732360 0.001453 0.000582 44NM_032047 B3GNT5 6.871 8.033 8.009 7.744 8.548 0.000000 1.0000000.438680 0.013149 0.000955 362 NM_032047 B3GNT5 6.871 8.033 8.009 7.7448.548 0.000000 1.000000 0.438680 0.013149 0.000955 229 NM_207647 FSD1L4.605 4.402 4.801 4.565 5.099 1.000000 0.000000 0.235142 0.0501770.001077 534 NM_207647 FSD1L 4.605 4.402 4.801 4.565 5.099 1.0000000.000000 0.235142 0.050177 0.001077 198 gi|21538810 NPCDR1 5.404 5.0224.784 5.166 4.817 0.000001 1.000000 0.000589 0.919120 0.001847 507gi|21538810 NPCDR1 5.404 5.022 4.784 5.166 4.817 0.000001 1.0000000.000589 0.919120 0.001847 220 NM_207627 ABCG1 8.318 7.923 7.960 8.2147.791 0.000000 1.000000 0.112606 0.270961 0.003184 526 NM_207627 ABCG18.318 7.923 7.960 8.214 7.791 0.000000 1.000000 0.112606 0.2709610.003184

TABLE 6 Shock versus Severe Area Under Curve (AUC) SEQ ID HC vs. Sick HCvs. SIRS HC vs. inSIRS vs. Mild vs. Mild vs. Severe vs. Number DatabaseID Gene Name AUC AUC ipSIRS AUC ipSIRS AUC Severe AUC Shock AUC ShockAUC 64 NM_004172 SLC1A3 0.954599761 0.985409652 0.943426943 0.6360306360.557823129 0.806530612 0.823129252 381 NM_004172 SLC1A3 0.9545997610.985409652 0.943426943 0.636030636 0.557823129 0.806530612 0.82312925279 NM_005546 ITK 0.888888889 0.873737374 0.894383394 0.5208125210.646258503 0.735510204 0.805442177 395 NM_005546 ITK 0.8888888890.873737374 0.894383394 0.520812521 0.646258503 0.735510204 0.80544217722 NM_005449 FAIM3 0.951612903 0.930415264 0.959299959 0.5960705960.57414966 0.749387755 0.793197279 340 NM_005449 FAIM3 0.9516129030.930415264 0.959299959 0.596070596 0.57414966 0.749387755 0.793197279171 NR_046000 IRF4 0.776433692 0.946127946 0.714896215 0.7412587410.759183673 0.604897959 0.782312925 NR_046000 IRF4 0.7764336920.946127946 0.714896215 0.741258741 0.759183673 0.604897959 0.78231292534 NM_001744 CAMK4 0.939217443 0.921436588 0.945665446 0.559773560.594557823 0.725714286 0.776870748 352 NM_001744 CAMK4 0.9392174430.921436588 0.945665446 0.55977356 0.594557823 0.725714286 0.776870748271 NM_173485 TSHZ2 0.705197133 0.733445567 0.694953195 0.5434565430.828571429 0.528979592 0.776870748 574 NM_173485 TSHZ2 0.7051971330.733445567 0.694953195 0.543456543 0.828571429 0.528979592 0.77687074888 NM_181506 LRRC70 0.795997611 0.49382716 0.901098901 0.8578088580.736054422 0.515102041 0.76462585 402 NM_181506 LRRC70 0.7959976110.49382716 0.901098901 0.857808858 0.736054422 0.515102041 0.76462585176 NM_002230 JUP 0.830346476 0.75308642 0.858363858 0.5730935730.500680272 0.765714286 0.760544218 485 NM_002230 JUP 0.8303464760.75308642 0.858363858 0.573093573 0.500680272 0.765714286 0.76054421844 NM_032047 B3GNT5 0.9369773 0.948933782 0.932641433 0.5487845490.565986395 0.692244898 0.759183673 362 NM_032047 B3GNT5 0.93697730.948933782 0.932641433 0.548784549 0.565986395 0.692244898 0.759183673235 NM_003531 HIST1H3C 0.781212664 0.881593715 0.744810745 0.6723276720.643537415 0.651428571 0.752380952 539 NM_003531 HIST1H3C 0.7812126640.881593715 0.744810745 0.672327672 0.643537415 0.651428571 0.752380952

Example 2 Differentiating inSIRS and ipSIRS in Post-Surgical and MedicalPatients

Surgical and medical patients often develop inSIRS post-surgery,post-procedural or as part of a co-morbidity or co-morbidities. Suchinpatients have a higher incidence of inSIRS and a higher risk ofdeveloping ipSIRS. Medical care in such patients therefore involvesmonitoring for signs of inSIRS and ipSIRS, differentiating between thesetwo conditions, and determining at the earliest possible time when apatient transitions from inSIRS to ipSIRS. The treatment and managementof inSIRS and ipSIRS patients is different, since inSIRS patients can beput on mild anti-inflammatory drugs or anti-pyretics and ipSIRS patientsmust be started on antibiotics as soon as possible for best outcomes.Monitoring post-surgical and medical patients on a regular basis withbiomarkers of the present invention will allow nursing and medicalpractitioners to differentiate inSIRS and ipSIRS at an early stage andhence make informed decisions on choice of therapies and patientmanagement procedures, and ultimately response to therapy. Informationprovided by these biomarkers will therefore allow medical practitionersto tailor and modify therapies to ensure patients recover quickly fromsurgery and do not develop ipSIRS. Less time in hospital and lesscomplications leads to considerable savings in medical expensesincluding through less occupancy time and appropriate use and timing ofmedications. Practical examples of the use of the biomarkers in Tables 7and 8 are described.

Table 7 lists the top 10 biomarkers (of 216) in order of ascending pvalue when comparing the two clinical groups of inSIRS and ipSIRS. A SEQID NO. is provided for each IRS biomarker (IRS biomarker polynucleotidesrange from SEQ ID NO. 1-319, IRS biomarker polypeptides range from SEQID No. 320-619), along with a database identification tag (e.g. NM_), agene name (Gene Name) if there is one, mean expression values forhealthy (HC), inSIRS, mild sepsis, severe sepsis and septic shock, and pvalues for HC vs. all other groups, inSIRS vs. ipSIRS, mild sepsisversus severe sepsis, mild sepsis versus septic shock and septic shockversus severe sepsis. All biomarkers have clinical utility indistinguishing inSIRS and ipSIRS and for distinguishing inSIRS andipSIRS as early as possible. Seven (7) of these biomarkers are alsouseful in distinguishing healthy control from sick although this has noclinical utility for post-surgical or medical patients. Some of thesebiomarkers also have limited utility in determining ipSIRS severity asindicated by respective p values less than 0.05. By example, in Table 7,inSIRS vs. ipSIRS p Value, it can be seen that the gene C11orf82 has asignificant p value for both inSIRS versus ipSIRS and Healthy versusother groups and therefore has utility in separating healthy and inSIRSpatients from septic patients. From the columns in the table containingmean expression data it can be seen that C11orf82 is down-regulated inboth inSIRS (5.888) and healthy controls (5.776) compared to septicpatients of all classes (mild (6.889), severe (7.153) and shock (7.293))(7.281) (also see FIG. 4).

Table 8 lists the top 10 biomarkers (of 104 with an AUC>0.8) in order ofdescending AUC when comparing the two clinical groups of inSIRS andipSIRS and it can be seen that C11orf82, PLAC8 and INSIG1 have AUCs of0.9477, 0.9210 and 0.9120, respectively (see also FIGS. 4, 5 and 6).

TABLE 7 inSIRS versus ipSIRS p Value pval pval HC pval pval Shock SEQ IDGene HC inSIRS Mild Severe Shock vs. pval inSIRS Severe Shock vs. NumberDatabase ID Name Mean Mean Mean Mean Mean Other vs. ipSIRS vs. Mild vs.Mild Severe 12 NM_145018 C11orf82 5.776 5.888 6.889 7.153 7.293 0.0000000.000000 0.322762 0.032568 0.722429 330 NM_145018 C11orf82 5.776 5.8886.889 7.153 7.293 0.000000 0.000000 0.322762 0.032568 0.722429 83NR_036641 PDGFC 6.098 6.117 6.987 7.044 7.466 0.000000 0.000000 0.9706370.064634 0.196970 NR_036641 PDGFC 6.098 6.117 6.987 7.044 7.466 0.0000000.000000 0.970637 0.064634 0.196970 106 NM_018375 SLC39A9 8.038 7.7198.121 8.368 8.428 1.000000 0.000000 0.034062 0.001276 0.808136 419NM_018375 SLC39A9 8.038 7.719 8.121 8.368 8.428 1.000000 0.0000000.034062 0.001276 0.808136 150 NM_030796 VOPP1 9.302 8.771 9.510 9.3189.517 1.000000 0.000000 0.298375 0.997162 0.269787 461 NM_030796 VOPP19.302 8.771 9.510 9.318 9.517 1.000000 0.000000 0.298375 0.9971620.269787 73 NM_001257400 CD63 9.235 9.126 9.718 9.990 10.159 0.0000000.000000 0.156468 0.002260 0.485665 389 NM_001257400 CD63 9.235 9.1269.718 9.990 10.159 0.000000 0.000000 0.156468 0.002260 0.485665 55NM_014143 CD274 5.508 5.656 7.557 7.211 7.237 0.000000 0.000000 0.5366620.490374 0.996684 372 NM_014143 CD274 5.508 5.656 7.557 7.211 7.2370.000000 0.000000 0.536662 0.490374 0.996684 111 NM_198336 INSIG1 8.0817.370 8.062 7.867 7.913 0.001237 0.000000 0.123875 0.197540 0.883915 424NM_198336 INSIG1 8.081 7.370 8.062 7.867 7.913 0.001237 0.0000000.123875 0.197540 0.883915 76 ENST00000443533 DDAH2 8.067 8.170 8.6308.707 9.015 0.000000 0.000000 0.868573 0.011535 0.108528 392ENST00000443533 DDAH2 8.067 8.170 8.630 8.707 9.015 0.000000 0.0000000.868573 0.011535 0.108528 115 NM_003546 HIST1H4L 9.807 7.908 9.4669.602 9.065 0.000032 0.000000 0.878290 0.231084 0.140998 428 NM_003546HIST1H4L 9.807 7.908 9.466 9.602 9.065 0.000032 0.000000 0.8782900.231084 0.140998 226 NM_003537 HIST1H3B 8.783 7.684 8.739 9.501 8.8521.000000 0.000000 0.042709 0.908040 0.098544 532 NM_003537 HIST1H3B8.783 7.684 8.739 9.501 8.852 1.000000 0.000000 0.042709 0.9080400.098544

TABLE 8 inSIRS versus ipSIRS Area Under Curve (AUC) SEQ ID HC vs. SickHC vs. SIRS HC vs. inSIRS vs. Mild vs. Mild vs. Severe vs. NumberDatabase ID Gene Name AUC AUC ipSIRS AUC ipSIRS AUC Severe AUC Shock AUCShock AUC 12 NM_145018 C11orf82 0.873058542 0.580246914 0.9792429790.947718948 0.619047619 0.650612245 0.555102041 330 NM_145018 C11orf820.873058542 0.580246914 0.979242979 0.947718948 0.619047619 0.6506122450.555102041 72 NM_001130715 PLAC8 0.635902031 0.828282828 0.8042328040.921078921 0.506122449 0.653061224 0.642176871 388 NM_001130715 PLAC80.635902031 0.828282828 0.804232804 0.921078921 0.506122449 0.6530612240.642176871 132 gi|21757933 NA 0.533004779 0.867564534 0.5883190880.912753913 0.708843537 0.631020408 0.540136054 445 gi|21757933 NA0.533004779 0.867564534 0.588319088 0.912753913 0.708843537 0.6310204080.540136054 111 NM_198336 INSIG1 0.695191159 0.957351291 0.60012210.912087912 0.666666667 0.631836735 0.518367347 424 NM_198336 INSIG10.695191159 0.957351291 0.6001221 0.912087912 0.666666667 0.6318367350.518367347 90 gi|21749325 CDS2 0.669354839 0.730078563 0.8142043140.907092907 0.586394558 0.56244898 0.504761905 gi|21749325 CDS20.669354839 0.730078563 0.814204314 0.907092907 0.586394558 0.562448980.504761905 150 NM_030796 VOPP1 0.53875448 0.937710438 0.6059218560.906759907 0.63537415 0.544489796 0.66122449 461 NM_030796 VOPP10.53875448 0.937710438 0.605921856 0.906759907 0.63537415 0.5444897960.66122449 106 NM_018375 SLC39A9 0.559587814 0.775533109 0.6811151810.901098901 0.730612245 0.735510204 0.557823129 419 NM_018375 SLC39A90.559587814 0.775533109 0.681115181 0.901098901 0.730612245 0.7355102040.557823129 37 NM_199135 FOXD4L3 0.815860215 0.49382716 0.9281644280.900765901 0.597278912 0.608163265 0.48707483 355 NM_199135 FOXD4L30.815860215 0.49382716 0.928164428 0.900765901 0.597278912 0.6081632650.48707483 68 NM_018639 WSB2 0.782108722 0.581369248 0.9139194140.9004329 0.555102041 0.533877551 0.530612245 384 NM_018639 WSB20.782108722 0.581369248 0.913919414 0.9004329 0.555102041 0.5338775510.530612245 73 NM_001257400 CD63 0.73655914 0.612233446 0.8630443630.897768898 0.644897959 0.72244898 0.613605442 389 NM_001257400 CD630.73655914 0.612233446 0.863044363 0.897768898 0.644897959 0.722448980.613605442

Example 3 Differentiating Both inSIRS and ipSIRS in Emergency DepartmentPatients and Determining Degree of Illness

Patients presenting to emergency departments often have a fever, whichis one (of four) of the clinical signs of inSIRS. Such patients need tobe assessed to determine if they have either inSIRS or ipSIRS. Furtherit is important to determine how sick they are to be able to make ajudgement call on whether to admit the patient or not. As mentionedabove, the treatment and management of pyretic, inSIRS and septicpatients are different. By way of example, a patient with a feverwithout other inSIRS clinical signs and no obvious source of infectionmay be sent home, or provided with other non-hospital services, withoutfurther hospital treatment. However, a patient with a fever may haveearly ipSIRS and not admitting such a patient may put their life atrisk. Because these biomarkers can differentiate inSIRS and ipSIRS anddetermine how sick a patient is they will allow medical practitioners totriage emergency department patients quickly and effectively. Accuratetriage decision-making insures that patients requiring hospitaltreatment are given it, and those that don't are provided with otherappropriate services. Practical examples of the use of the biomarkers inTables 9 and 10 are described.

Table 9 lists 30 significant biomarkers when comparing the groups ofhealthy and sick (sick consisting of those patients with either inSIRSor ipSIRS) and inSIRS versus ipSIRS. A SEQ ID NO. is provided for eachIRS biomarker (IRS biomarker polynucleotides range from SEQ ID NO.1-319, IRS biomarker polypeptides range from SEQ ID No. 320-619), alongwith a database identification tag (e.g. NM_), a gene name (Gene Name)if there is one, mean expression values for healthy (HC), inSIRS, mildsepsis, severe sepsis and septic shock, and p values for HC vs. allother groups, inSIRS vs. ipSIRS, mild sepsis versus severe sepsis, mildsepsis versus septic shock and septic shock versus severe sepsis. Suchbiomarkers have clinical utility in distinguishing healthy from sickpatients and inSIRS from ipSIRS patients. By example, in Table 9,Healthy versus inSIRS versus ipSIRS, it can be seen that the gene FCGR1Ahas a significant p value for both inSIRS versus ipSIRS and Healthyversus other groups and therefore has utility in separating healthy andinSIRS and ipSIRS patients. From the columns in the table containingmean expression data it can be seen that FCGR1A is up-regulated ininSIRS (9.281) compared to healthy controls (7.871) but more so inipSIRS patients (9.985-10.308). Such a upward gradient in geneexpression can be used to determine the degree of illness in patientspresenting to an emergency department allowing clinicians to riskstratify and triage with greater certainty (see also FIG. 7).

Table 10 lists 10 significant biomarkers when comparing the groups ofhealthy and sick (sick consisting of those patients with either inSIRSor ipSIRS) and inSIRS versus ipSIRS. A SEQ ID NO. is provided for eachIRS biomarker (IRS biomarker polynucleotides range from SEQ ID NO.1-319, IRS biomarker polypeptides range from SEQ ID No. 320-619), alongwith a database identification tag (e.g. NM_), a gene name (Gene Name)if there is one, mean expression values for healthy (HC), inSIRS, mildsepsis, severe sepsis and septic shock, and p values for HC vs. allother groups, inSIRS vs. ipSIRS, mild sepsis versus severe sepsis, mildsepsis versus septic shock and septic shock versus severe sepsis. Suchbiomarkers have clinical utility in distinguishing healthy from sickpatients and inSIRS from ipSIRS patients. By example, in Table 10,Healthy versus inSIRS versus ipSIRS, it can be seen that the gene CHI3L1has a significant p value for both inSIRS versus ipSIRS and Healthyversus other groups and therefore has utility in separating healthy andinSIRS and septic patients. From the columns in the table containingmean expression data it can be seen that CHI3L1 is down-regulated ininSIRS (9.876) compared to healthy controls (10.47) but more so inipSIRS patients (8.64-9.035). Such a downward gradient in geneexpression can be used to determine the degree of illness in patientspresenting to an emergency department allowing clinicians to riskstratify and triage with greater certainty (see also FIG. 8).

TABLE 9 Healthy versus inSIRS versus ipSIRS p Value pval pval pval pvalSEQ ID inSIRS Mild Severe Shock pval HC inSIRS vs. Severe vs. Shock vs.Shock vs. Number Database ID Gene Name HC Mean Mean Mean Mean Mean vs.Other ipSIRS Mild Mild Severe 11 NR_045213 FCGR1A 7.871 9.281 10.3089.985 10.273 0.000000 0.001046 0.284201 0.980298 0.366022 Non- NR_045213FCGR1A 7.871 9.281 10.308 9.985 10.273 0.000000 0.001046 0.2842010.980298 0.366022 coding 20 NM_153046 TDRD9 4.986 5.567 6.483 6.9377.385 0.000000 0.000000 0.248195 0.001153 0.259068 338 NM_153046 TDRD94.986 5.567 6.483 6.937 7.385 0.000000 0.000000 0.248195 0.0011530.259068 29 NM_020370 GPR84 6.712 8.157 9.030 8.980 9.583 0.0000000.001894 0.989573 0.184680 0.221197 347 NM_020370 GPR84 6.712 8.1579.030 8.980 9.583 0.000000 0.001894 0.989573 0.184680 0.221197 25NM_018367 ACER3 7.317 7.845 8.701 8.417 9.050 0.000000 0.000000 0.3629610.132905 0.008450 343 NM_018367 ACER3 7.317 7.845 8.701 8.417 9.0500.000000 0.000000 0.362961 0.132905 0.008450 86 NM_000860 HPGD 5.6216.238 7.085 6.908 7.946 0.000000 0.000025 0.895298 0.035905 0.027021 400NM_000860 HPGD 5.621 6.238 7.085 6.908 7.946 0.000000 0.000025 0.8952980.035905 0.027021 65 NM_006418 OLFM4 6.365 7.209 8.023 9.641 9.3220.000000 0.000069 0.003150 0.006691 0.784808 382 NM_006418 OLFM4 6.3657.209 8.023 9.641 9.322 0.000000 0.000069 0.003150 0.006691 0.784808 8NM_004054 C3AR1 8.429 9.449 10.261 10.439 10.593 0.000000 0.0000160.650271 0.142678 0.725241 327 NM_004054 C3AR1 8.429 9.449 10.261 10.43910.593 0.000000 0.000016 0.650271 0.142678 0.725241 6 NM_002934 RNASE29.164 10.500 11.243 11.670 11.388 0.000000 0.002979 0.095954 0.6909760.351809 325 NM_002934 RNASE2 9.164 10.500 11.243 11.670 11.388 0.0000000.002979 0.095954 0.690976 0.351809 21 NM_032045 KREMEN1 8.626 9.40910.143 10.189 10.055 0.000000 0.000079 0.962640 0.837239 0.731337 339NM_032045 KREMEN1 8.626 9.409 10.143 10.189 10.055 0.000000 0.0000790.962640 0.837239 0.731337 1 NM_003268 TLR5 7.747 9.010 9.726 9.97910.311 0.000000 0.000225 0.275728 0.000244 0.110996 320 NM_003268 TLR57.747 9.010 9.726 9.979 10.311 0.000000 0.000225 0.275728 0.0002440.110996 280 NM_153021 PLB1 8.205 8.887 9.574 9.463 10.019 0.0000000.000133 0.872838 0.059398 0.037699 582 NM_153021 PLB1 8.205 8.887 9.5749.463 10.019 0.000000 0.000133 0.872838 0.059398 0.037699 15 NM_004482GALNT3 5.685 6.251 6.916 6.728 7.075 0.000000 0.000000 0.407446 0.4228010.051201 333 NM_004482 GALNT3 5.685 6.251 6.916 6.728 7.075 0.0000000.000000 0.407446 0.422801 0.051201 161 NM_001816 CEACAM8 7.336 7.8748.503 9.775 9.287 0.000000 0.001298 0.011921 0.098854 0.501991 472NM_001816 CEACAM8 7.336 7.874 8.503 9.775 9.287 0.000000 0.0012980.011921 0.098854 0.501991 36 NM_007115 TNFAIP6 7.738 9.246 9.829 9.6319.738 0.000000 1.000000 0.712067 0.908467 0.905260 354 NM_007115 TNFAIP67.738 9.246 9.829 9.631 9.738 0.000000 1.000000 0.712067 0.9084670.905260 4 NM_016021 UBE2J1 8.792 9.555 10.118 10.044 10.335 0.0000000.000015 0.817659 0.104460 0.049488 323 NM_016021 UBE2J1 8.792 9.55510.118 10.044 10.335 0.000000 0.000015 0.817659 0.104460 0.049488 35NM_015268 DNAJC13 7.507 8.083 8.596 8.693 8.878 0.000000 0.0000000.833718 0.133216 0.512817 353 NM_015268 DNAJC13 7.507 8.083 8.596 8.6938.878 0.000000 0.000000 0.833718 0.133216 0.512817

TABLE 10 Healthy versus inSIRS versus ipSIRS p Value SEQ ID pval pvalpval pval Num- HC inSIRS Mild Severe Shock pval HC inSIRS Severe vs.Shock Shock vs. ber Database ID Gene Name Mean Mean Mean Mean Mean vs.Other vs. ipSIRS Mild vs. Mild Severe 104 NM_001276 CHI3L1 10.470 9.8768.640 9.035 8.726 0.000000 0.000056 0.485576 0.954853 0.641602 417NM_001276 CHI3L1 10.470 9.876 8.640 9.035 8.726 0.000000 0.0000560.485576 0.954853 0.641602 122 NM_001143804 PHOSPHO1 11.398 10.82610.374 9.837 10.185 0.000000 0.048380 0.088690 0.661703 0.354179 435NM_001143804 PHOSPHO1 11.398 10.826 10.374 9.837 10.185 0.0000000.048380 0.088690 0.661703 0.354179 40 NM_016523 KLRF1 6.343 5.438 5.0224.504 4.543 0.000000 0.007278 0.033428 0.021421 0.979558 358 NM_016523KLRF1 6.343 5.438 5.022 4.504 4.543 0.000000 0.007278 0.033428 0.0214210.979558 33 NM_000953 PTGDR 9.310 8.373 8.028 7.790 7.577 0.0000000.007043 0.500548 0.040553 0.570947 351 NM_000953 PTGDR 9.310 8.3738.028 7.790 7.577 0.000000 0.007043 0.500548 0.040553 0.570947 103ENST00000381907 KLRD1 8.651 8.097 7.766 7.201 7.123 0.000000 0.0118380.056985 0.008125 0.944270 416 ENST00000381907 KLRD1 8.651 8.097 7.7667.201 7.123 0.000000 0.011838 0.056985 0.008125 0.944270

Example 4 Differentiating Healthy from Sick Patients and DeterminingDegree of Illness

Patients presenting to medical clinics often have any one of the fourclinical signs of inSIRS (increased heart rate, increased respiratoryrate, abnormal white blood cell count, fever or hypothermia). Manydifferent clinical conditions can present with one of the four clinicalsigns of inSIRS and such patients need to be assessed to determine ifthey have either inSIRS or ipSIRS and to exclude other differentialdiagnoses. By way of example, a patient with colic might also presentwith clinical signs of increased heart rate. Differential diagnosescould be (but not limited to) appendicitis, urolithiasis, cholecystitis,pancreatitis, enterocolitis. In each of these conditions it would beimportant to determine if there was a systemic inflammatory response(inSIRS) or whether an infection was contributing to the condition. Thetreatment and management of patients with and without systemicinflammation and/or infection are different. Because these biomarkerscan differentiate healthy from sick (inSIRS and ipSIRS), and determinethe degree of systemic involvement, the use of them will allow medicalpractitioners to determine the next medical procedure(s) to perform tosatisfactorily resolve the patient issue. Practical examples of the useof the biomarkers in Tables 11, 12, 13 and 14 are described.

Table 11 lists 20 significant biomarkers (of 150) when comparing thegroups of healthy and sick (sick consisting of those patients witheither inSIRS or ipSIRS). A SEQ ID NO. is provided for each IRSbiomarker (IRS biomarker polynucleotides range from SEQ ID NO. 1-319,IRS biomarker polypeptides range from SEQ ID No. 320-619), along with adatabase identification tag (e.g. NM_), a gene name (Gene Name) if thereis one, mean expression values for healthy (HC), inSIRS, mild sepsis,severe sepsis and septic shock, and p values for HC vs. all othergroups, inSIRS vs. ipSIRS, mild sepsis versus severe sepsis, mild sepsisversus septic shock and septic shock versus severe sepsis. Suchbiomarkers have clinical utility in distinguishing healthy from sickpatients and determining the level of systemic inflammation and/orinfection. For example, in Table 11, Healthy versus Sick, it can be seenthat the gene CD177 has a significant p value for healthy control versusother groups and therefore has utility in separating healthy and sickpatients. From the columns in the table containing mean expression datait can be seen that CD177 is up-regulated in inSIRS (10.809) compared tohealthy controls (8.091) but more so in ipSIRS patients (11.267-12.088).Such up-regulated differences in gene expression can be used todetermine the degree of systemic inflammation and infection in patientspresenting to clinics allowing clinicians to more easily determine thenext medical procedure(s) to perform to satisfactorily resolve thepatient issue (see also FIG. 9).

Further, and by example, in Table 11, Healthy versus Sick, it can beseen that the gene GNLY has a significant p value for healthy controlversus other groups and therefore has utility in separating healthy andsick patients. From the columns in the table containing mean expressiondata it can be seen that GNLY is down-regulated in inSIRS (9.428)compared to healthy controls (10.653) but more so in septic patients(9.305-8.408). GNLY has an AUC of 0.9445 (not shown) for separatinghealthy and sick patients. Such down-regulated differences in geneexpression can be used to determine the degree of systemic inflammationand infection in patients presenting to clinics allowing clinicians tomore easily determine the next medical procedure(s) to perform tosatisfactorily resolve the patient issue (see also FIG. 10).

Table 12 lists the top 10 biomarkers (of 118 with an AUC of at least0.8) for separating healthy from sick patients (sick being thosepatients with either inSIRS or ipSIRS) by decreasing value of Area UnderCurve (AUC). It can be seen that the highest AUC is for CD177 forseparating healthy from sick (0.9929) (see also FIG. 9).

Table 13 lists the top 10 biomarkers (of 152 with an AUC of at least0.8) for separating healthy from inSIRS patients by decreasing value ofArea Under Curve (AUC). It can be seen that the highest AUC is for BMXfor separating healthy from inSIRS (1). That is, this biomarker alonecan perfectly separate these two groups (see also FIG. 11).

Table 14 lists the top 10 biomarkers (of 140 with an AUC of at least0.8) for separating healthy from ipSIRS patients by decreasing value ofArea Under Curve (AUC). It can be seen that the highest AUC is for TLR5for separating healthy from ipSIRS (0.9945) (see also FIG. 12).

TABLE 11 Healthy versus Sick p Value pval SIRS pval pval pval SEQ IDGene HC SIRS Mild Severe Shock pval HC vs. Severe Shock vs. Shock vs.Number Database ID Name Mean Mean Mean Mean Mean vs. Other ipSIRS vs.Mild Mild Severe 2 NM_020406 CD177 8.091 10.809 11.267 12.088 12.0440.000000 0.087061 0.048910 0.027926 0.991139 321 NM_020406 CD177 8.09110.809 11.267 12.088 12.044 0.000000 0.087061 0.048910 0.027926 0.99113910 NM_001244438 ARG1 5.410 9.054 7.895 8.254 8.919 0.000000 1.0000000.628534 0.008931 0.209877 329 NM_001244438 ARG1 5.410 9.054 7.895 8.2548.919 0.000000 1.000000 0.628534 0.008931 0.209877 3 NM_004666 VNN17.736 10.013 10.007 10.629 10.876 0.000000 1.000000 0.087388 0.0024020.671136 322 NM_004666 VNN1 7.736 10.013 10.007 10.629 10.876 0.0000001.000000 0.087388 0.002402 0.671136 7 NM_080387 CLEC4D 7.187 9.915 9.2389.152 9.828 0.000000 0.383427 0.945300 0.034026 0.035853 326 NM_080387CLEC4D 7.187 9.915 9.238 9.152 9.828 0.000000 0.383427 0.945300 0.0340260.035853 29 NM_020370 GPR84 6.712 8.157 9.030 8.980 9.583 0.0000000.001894 0.989573 0.184680 0.221197 347 NM_020370 GPR84 6.712 8.1579.030 8.980 9.583 0.000000 0.001894 0.989573 0.184680 0.221197 24NM_003855 IL18R1 5.516 8.101 7.098 7.538 8.097 0.000000 1.0000000.373616 0.001873 0.205385 342 NM_003855 IL18R1 5.516 8.101 7.098 7.5388.097 0.000000 1.000000 0.373616 0.001873 0.205385 65 NM_006418 OLFM46.365 7.209 8.023 9.641 9.322 0.000000 0.000068 0.003150 0.0066910.784808 382 NM_006418 OLFM4 6.365 7.209 8.023 9.641 9.322 0.0000000.000068 0.003150 0.006691 0.784808 11 NR_045213 FCGR1A 7.871 9.28110.308 9.985 10.273 0.000000 0.001046 0.284201 0.980298 0.366022NR_045213 FCGR1A 7.871 9.281 10.308 9.985 10.273 0.000000 0.0010460.284201 0.980298 0.366022 6 NM_002934 RNASE2 9.164 10.500 11.243 11.67011.388 0.000000 0.002979 0.095954 0.690976 0.351809 325 NM_002934 RNASE29.164 10.500 11.243 11.670 11.388 0.000000 0.002979 0.095954 0.6909760.351809 14 NM_006433 GNLY 10.653 9.428 9.305 8.659 8.408 0.0000000.020098 0.014566 0.000045 0.511511 332 NM_006433 GNLY 10.653 9.4289.305 8.659 8.408 0.000000 0.020098 0.014566 0.000045 0.511511

TABLE 12 Healthy versus Sick Area Under Curve (AUC) SEQ ID Num- Gene HCvs. Sick HC vs. HC vs. SIRS vs. Mild vs. Mild vs. Severe vs. berDatabase ID Name AUC inSIRS AUC ipSIRS AUC ipSIRS AUC Severe AUC ShockAUC Shock AUC 2 NM_020406 CD177 0.992980884 0.991582492 0.9934879930.718281718 0.668027211 0.675102041 0.540136054 321 NM_020406 CD1770.992980884 0.991582492 0.993487993 0.718281718 0.668027211 0.6751020410.540136054 7 NM_080387 CLEC4D 0.981780167 0.998877666 0.9755799760.64968365 0.52244898 0.671020408 0.691156463 326 NM_080387 CLEC4D0.981780167 0.998877666 0.975579976 0.64968365 0.52244898 0.6710204080.691156463 18 NM_203281 BMX 0.979988053 1 0.972730973 0.560439560.639455782 0.749387755 0.644897959 336 NM_203281 BMX 0.979988053 10.972730973 0.56043956 0.639455782 0.749387755 0.644897959 3 NM_004666VNN1 0.979241338 0.996632997 0.972934473 0.663003663 0.6489795920.710204082 0.575510204 322 NM_004666 VNN1 0.979241338 0.9966329970.972934473 0.663003663 0.648979592 0.710204082 0.575510204 29 NM_020370GPR84 0.974313023 0.92704826 0.991452991 0.738927739 0.4965986390.608163265 0.623129252 347 NM_020370 GPR84 0.974313023 0.927048260.991452991 0.738927739 0.496598639 0.608163265 0.623129252 10NM_001244438 ARG1 0.970878136 0.999438833 0.960520961 0.6443556440.561904762 0.683265306 0.662585034 329 NM_001244438 ARG1 0.9708781360.999438833 0.960520961 0.644355644 0.561904762 0.683265306 0.66258503424 NM_003855 IL18R1 0.966845878 0.989337823 0.958689459 0.629703630.62585034 0.715102041 0.639455782 342 NM_003855 IL18R1 0.9668458780.989337823 0.958689459 0.62970363 0.62585034 0.715102041 0.639455782 26NM_006459 ERLIN1 0.964755078 0.994949495 0.953805454 0.6949716950.561904762 0.639183673 0.594557823 344 NM_006459 ERLIN1 0.9647550780.994949495 0.953805454 0.694971695 0.561904762 0.639183673 0.5945578235 NM_018285 IMP3 0.96385902 0.997755331 0.951566952 0.8175158180.610884354 0.742040816 0.614965986 324 NM_018285 IMP3 0.963859020.997755331 0.951566952 0.817515818 0.610884354 0.742040816 0.6149659861 NM_003268 TLR5 0.962365591 0.873737374 0.994505495 0.8085248090.606802721 0.768979592 0.672108844 320 NM_003268 TLR5 0.9623655910.873737374 0.994505495 0.808524809 0.606802721 0.768979592 0.672108844

TABLE 13 Healthy versus inSIRS Area Under Curve (AUC) SEQ ID HC vs. SickHC vs. inSIRS HC vs. ipSIRS SIRS vs. Mild vs. Mild vs. Severe vs. NumberDatabase ID Gene Name AUC AUC AUC ipSIRS AUC Severe AUC Shock AUC ShockAUC 18 NM_203281 BMX 0.979988053 1 0.972730973 0.56043956 0.6394557820.749387755 0.644897959 336 NM_203281 BMX 0.979988053 1 0.9727309730.56043956 0.639455782 0.749387755 0.644897959 10 NM_001244438 ARG10.970878136 0.999438833 0.960520961 0.644355644 0.561904762 0.6832653060.662585034 329 NM_001244438 ARG1 0.970878136 0.999438833 0.9605209610.644355644 0.561904762 0.683265306 0.662585034 7 NM_080387 CLEC4D0.981780167 0.998877666 0.975579976 0.64968365 0.52244898 0.6710204080.691156463 326 NM_080387 CLEC4D 0.981780167 0.998877666 0.9755799760.64968365 0.52244898 0.671020408 0.691156463 5 NM_018285 IMP30.96385902 0.997755331 0.951566952 0.817515818 0.610884354 0.7420408160.614965986 324 NM_018285 IMP3 0.96385902 0.997755331 0.9515669520.817515818 0.610884354 0.742040816 0.614965986 3 NM_004666 VNN10.979241338 0.996632997 0.972934473 0.663003663 0.648979592 0.7102040820.575510204 322 NM_004666 VNN1 0.979241338 0.996632997 0.9729344730.663003663 0.648979592 0.710204082 0.575510204 26 NM_006459 ERLIN10.964755078 0.994949495 0.953805454 0.694971695 0.561904762 0.6391836730.594557823 344 NM_006459 ERLIN1 0.964755078 0.994949495 0.9538054540.694971695 0.561904762 0.639183673 0.594557823 17 NM_207113 SLC37A30.954301075 0.99382716 0.93996744 0.582417582 0.619047619 0.6530612240.551020408 335 NM_207113 SLC37A3 0.954301075 0.99382716 0.939967440.582417582 0.619047619 0.653061224 0.551020408 38 NM_004994 MMP90.935782557 0.993265993 0.914936915 0.625374625 0.653061224 0.6538775510.48707483 356 NM_004994 MMP9 0.935782557 0.993265993 0.9149369150.625374625 0.653061224 0.653877551 0.48707483 120 NM_004244 CD1630.842293907 0.993265993 0.787545788 0.716949717 0.481632653 0.6636734690.648979592 433 NM_004244 CD163 0.842293907 0.993265993 0.7875457880.716949717 0.481632653 0.663673469 0.648979592 46 NM_006212 PFKFB20.922341697 0.992704826 0.896825397 0.678654679 0.51292517 0.6791836730.68707483 363 NM_006212 PFKFB2 0.922341697 0.992704826 0.8968253970.678654679 0.51292517 0.679183673 0.68707483

TABLE 14 Healthy versus ipSIRS Area Under Curve (AUC) SEQ ID HC vs. SickHC vs. HC vs. SIRS vs. Mild vs. Mild vs. Severe vs. Number Database IDGene Name AUC inSIRS AUC ipSIRS AUC ipSIRS AUC Severe AUC Shock AUCShock AUC 1 NM_003268 TLR5 0.962365591 0.873737374 0.9945054950.808524809 0.606802721 0.768979592 0.672108844 320 NM_003268 TLR50.962365591 0.873737374 0.994505495 0.808524809 0.606802721 0.7689795920.672108844 2 NM_020406 CD177 0.992980884 0.991582492 0.9934879930.718281718 0.668027211 0.675102041 0.540136054 321 NM_020406 CD1770.992980884 0.991582492 0.993487993 0.718281718 0.668027211 0.6751020410.540136054 29 NM_020370 GPR84 0.974313023 0.92704826 0.9914529910.738927739 0.496598639 0.608163265 0.623129252 347 NM_020370 GPR840.974313023 0.92704826 0.991452991 0.738927739 0.496598639 0.6081632650.623129252 20 NM_153046 TDRD9 0.929062127 0.758136925 0.9910459910.844488844 0.640816327 0.734693878 0.636734694 338 NM_153046 TDRD90.929062127 0.758136925 0.991045991 0.844488844 0.640816327 0.7346938780.636734694 4 NM_016021 UBE2J1 0.959677419 0.888327722 0.9855514860.826173826 0.468027211 0.644897959 0.682993197 323 NM_016021 UBE2J10.959677419 0.888327722 0.985551486 0.826173826 0.468027211 0.6448979590.682993197 11 NR_045213 FCGR1A 0.954749104 0.87037037 0.9853479850.77988678 0.612244898 0.492244898 0.621768707 NR_045213 FCGR1A0.954749104 0.87037037 0.985347985 0.77988678 0.612244898 0.4922448980.621768707 6 NM_002934 RNASE2 0.94937276 0.854657688 0.9837199840.780552781 0.67755102 0.564081633 0.643537415 325 NM_002934 RNASE20.94937276 0.854657688 0.983719984 0.780552781 0.67755102 0.5640816330.643537415 8 NM_004054 C3AR1 0.950119474 0.868125701 0.979853480.832833833 0.529251701 0.609795918 0.582312925 327 NM_004054 C3AR10.950119474 0.868125701 0.97985348 0.832833833 0.529251701 0.6097959180.582312925 12 NM_145018 C11orf82 0.873058542 0.580246914 0.9792429790.947718948 0.619047619 0.650612245 0.555102041 330 NM_145018 C11orf820.873058542 0.580246914 0.979242979 0.947718948 0.619047619 0.6506122450.555102041 13 NM_018099 FAR2 0.942502987 0.843434343 0.9784289780.794205794 0.561904762 0.746122449 0.693877551 331 NM_018099 FAR20.942502987 0.843434343 0.978428978 0.794205794 0.561904762 0.7461224490.693877551

Example 5 Differential Expression of IRS Biomarkers Markers BetweenHealthy, inSIRS, Mild Sepsis, Severe Sepsis and Septic Shock

Presented below in FIGS. 13 to 331 are “Box and Whisker” plots for eachof the 319 biomarkers where the bottom and top of the box are the firstand third quartiles, and the band inside the box is the second quartile(the median) (of gene expression). Biomarkers are presented in order ofascending adjusted p value when comparing “All Classes” (i.e., healthycontrol, referred to as “Healthy” in FIGS. 13-331; inSIRS, referred toas “SIRS” in FIGS. 13-331; mild sepsis referred to as “Mild” in FIGS.13-331; severe sepsis, referred to as “Severe” in FIGS. 13-331; andseptic shock, referred to as “Shock” in FIGS. 13-331)—varying from6.49E-48 to 1.00, and according to the following table (Table 15).Appropriate choice and use of such markers can be used to selectpatients for inclusion in, or exclusion from, clinical trials. Further,such markers can be used to determine the efficacy of treatment,therapies or management regimens in patients by determining whether apatient has transitioned from one condition to another and bydetermining the stage or degree of a particular condition. For example,an exemplary clinical trial design testing for the efficacy of aninotrope may include only those patients with shock ipSIRS that are mostlikely to best respond to such a drug. In addition, and followinginclusion of such patients and treatment with the inotrope, suchpatients could be monitored to determine if, when, how quickly and towhat degree they respond to the inotrope by their transition from shockipSIRS to other degrees of ipSIRS, inSIRS or health. Similarly, a modelclinical trial design testing for the efficacy of an antibiotic, orcombination of antibiotics, may include only those patients with ipSIRS,and not inSIRS, that are most likely to best respond to such a drug. Inaddition, and following inclusion of such patients and treatment withthe antibiotic(s), such patients could be monitored to determine if,when, how quickly and to what degree they respond to the antibiotic(s)by their transition from ipSIRS to inSIRS or health. Similarly, anexemplary clinical trial design testing for the efficacy of an immunemodulating drug (e.g. a steroid) may include only those patients withknown stages of ipSIRS, for example those recovering from ipSIRS orthose in the early stages of ipSIRS. Following inclusion of suchpatients and treatment with the immune modulating drug, such patientscould be monitored to determine if, when, how quickly and to what degreethey respond to the immune modulating drug by their transition fromipSIRS to inSIRS or health. The biomarker response and outcome (e.g.reduced length of hospital stay, reduced mortality) of patients invarious stages of ipSIRS (early, late) treated with an immune modulatingdrug may also indicate when such a drug is best administered for maximumbenefit.

TABLE 15 Healthy versus inSIRS versus ipSIRS versus Mild versus Severeversus Shock p Value and Area Under Curve (AUC) SEQ ID HC inSIRS MildSevere Number Database ID Gene Name Mean Mean Mean Mean 1 NM_003268 TLR57.747 9.010 9.726 9.979 2 NM_020406 CD177 8.091 10.809 11.267 12.088 3NM_004666 VNN1 7.736 10.013 10.007 10.629 4 NM_016021 UBE2J1 8.792 9.55510.118 10.044 5 NM_018285 IMP3 7.951 6.465 7.032 6.934 6 NM_002934RNASE2 9.164 10.500 11.243 11.670 7 NM_080387 CLEC4D 7.187 9.915 9.2389.152 8 NM_004054 C3AR1 8.429 9.449 10.261 10.439 9 NM_001145772 GPR569.741 8.456 8.297 7.926 10 NM_001244438 ARG1 5.410 9.054 7.895 8.254 11NR_045213 FCGR1A 7.871 9.281 10.308 9.985 12 NM_145018 C11orf82 5.7765.888 6.889 7.153 13 NM_018099 FAR2 8.164 8.881 9.322 9.439 14 NM_006433GNLY 10.653 9.428 9.305 8.659 15 NM_004482 GALNT3 5.685 6.251 6.9166.728 16 NM_002544 OMG 4.756 5.187 5.644 5.799 17 NM_207113 SLC37A38.600 10.048 9.633 9.916 18 NM_203281 BMX 4.804 6.547 6.012 6.397 19NM_004099 STOM 9.914 10.377 10.824 10.894 20 NM_153046 TDRD9 4.986 5.5676.483 6.937 21 NM_032045 KREMEN1 8.626 9.409 10.143 10.189 22 NM_005449FAIM3 10.259 9.101 9.036 9.174 23 NM_014358 CLEC4E 8.446 10.547 9.4919.618 24 NM_003855 IL18R1 5.516 8.101 7.098 7.538 25 NM_018367 ACER37.317 7.845 8.701 8.417 26 NM_006459 ERLIN1 7.136 9.162 8.418 8.526 27NM_004612 TGFBR1 9.328 9.614 10.165 9.999 28 NM_001145775 FKBP5 9.00611.106 10.185 10.457 29 NM_020370 GPR84 6.712 8.157 9.030 8.980 30NM_182597 C7orf53 6.397 6.878 7.266 7.760 31 NM_153021 PLB1 8.205 8.8879.574 9.463 32 NM_013352 DSE 7.272 7.680 8.183 8.109 33 NM_000953 PTGDR9.310 8.373 8.028 7.790 34 NM_001744 CAMK4 8.155 6.723 6.902 7.152 35NM_015268 DNAJC13 7.507 8.083 8.596 8.693 36 NM_007115 TNFAIP6 7.7389.246 9.829 9.631 37 NM_199135 FOXD4L3 6.441 6.501 7.402 7.610 38NM_004994 MMP9 10.179 12.012 11.383 11.801 39 NM_000637 GSR 8.866 9.3229.492 10.037 40 NM_016523 KLRF1 6.343 5.438 5.022 4.504 41 NM_053282SH2D1B 8.067 6.992 6.896 6.451 42 NM_001004441 ANKRD34B 4.809 5.4155.855 6.471 43 NM_001136258 SGMS2 6.693 7.708 7.553 7.712 44 NM_032047B3GNT5 6.871 8.033 8.009 7.744 45 NR_026575 GK3P 4.227 5.729 5.316 5.55246 NM_006212 PFKFB2 7.955 10.444 9.336 9.326 47 NM_007166 PICALM 9.0799.433 9.822 9.993 48 NM_152637 METTL7B 6.693 7.153 7.628 7.927 49NM_003542 HIST1H4C 11.803 9.795 10.815 10.617 50 NM_145005 C9orf72 8.2628.742 9.312 9.073 51 NM_003533 HIST1H3I 10.878 9.003 10.144 10.141 52NM_021082 SLC15A2 7.246 7.309 7.861 8.034 53 NM_030956 TLR10 6.794 6.8427.713 7.823 54 NM_001124 ADM 8.676 8.896 9.739 9.441 55 NM_014143 CD2745.508 5.656 7.557 7.211 56 NM_001311 CRIP1 8.880 6.932 7.984 7.844 57NM_001099660 LRRN3 7.163 5.997 5.841 6.367 58 NM_002121 HLA-DPB1 11.41410.665 10.623 9.971 59 NM_014232 VAMP2 9.213 8.896 9.016 8.454 60NM_006714 SMPDL3A 6.243 6.701 7.288 7.563 61 NM_005531 IFI16 8.97310.323 9.950 9.942 62 NM_016475 JKAMP 8.440 8.442 9.231 8.827 63ENST00000371443 MRPL41 8.037 6.496 7.288 7.313 64 NM_004172 SLC1A3 5.8496.892 6.472 6.447 65 NM_006418 OLFM4 6.365 7.209 8.023 9.641 66NM_001164116 CASS4 8.124 7.848 7.410 7.217 67 ENST00000533734 TCN1 6.0156.936 7.241 8.481 68 NM_018639 WSB2 8.870 8.808 9.618 9.714 69ENST00000405140 CLU 9.016 9.264 9.889 10.137 70 NM_001163278 ODZ1 6.0887.677 6.668 7.303 71 NM_002269 KPNA5 6.822 5.908 6.451 5.758 72NM_001130715 PLAC8 10.873 10.024 11.434 11.500 73 NM_001257400 CD639.235 9.126 9.718 9.990 74 NM_006665 HPSE 8.103 8.173 9.127 9.174 75NM_152367 C1orf161 5.851 6.354 6.479 6.738 76 ENST00000443533 DDAH28.067 8.170 8.630 8.707 77 NM_001199805 KLRK1 8.677 7.641 7.492 7.142 78NM_024524 ATP13A3 7.668 7.763 8.429 8.547 79 NM_005546 ITK 9.271 8.0998.227 8.536 80 NM_021127 PMAIP1 6.940 5.860 6.770 6.251 81 NR_046099LOC284757 6.913 8.028 6.894 7.534 82 NM_002080 GOT2 6.854 5.823 6.0176.378 83 NR_036641 PDGFC 6.098 6.117 6.987 7.044 84 NM_012200 B3GAT37.939 7.030 7.516 7.658 85 NM_003545 HIST1H4E 10.534 9.717 9.907 9.36286 NM_000860 HPGD 5.621 6.238 7.085 6.908 87 NM_031950 FGFBP2 8.0907.266 7.130 6.722 88 NM_181506 LRRC70 3.455 3.495 4.144 3.763 89NM_018342 TMEM144 5.697 6.377 6.781 7.043 90 gi|21749325 CDS2 10.2359.988 10.651 10.561 91 NM_001725 BPI 7.724 8.603 8.894 10.119 92ENST00000379215 ECHDC3 7.486 8.705 7.801 7.715 93 NM_001837 CCR3 7.0786.226 6.015 6.079 94 NM_014181 HSPC159 9.120 9.779 9.933 10.248 95NM_018324 OLAH 4.483 6.220 5.483 6.162 96 NM_006243 PPP2R5A 8.141 9.0008.646 8.931 97 NM_001193451 TMTC1 6.316 7.153 7.497 7.783 98NM_001023570 EAF2 8.389 8.607 9.323 9.016 99 NM_001268 RCBTB2 8.6468.621 9.321 9.357 100 NM_021982 SEC24A 7.847 7.914 8.266 8.571 101NM_001017995 SH3PXD2B 5.800 6.949 6.262 6.384 102 NM_001130688 HMGB27.782 9.225 8.769 8.869 103 ENST00000381907 KLRD1 8.651 8.097 7.7667.201 104 NM_001276 CHI3L1 10.470 9.876 8.640 9.035 105 NM_174938 FRMD36.218 6.408 6.959 6.889 106 NM_018375 SLC39A9 8.038 7.719 8.121 8.368107 NM_153236 GIMAP7 9.533 8.865 8.974 8.682 108 NM_016476 ANAPC11 6.7165.940 6.219 6.214 109 NM_019037 EXOSC4 8.216 8.199 8.716 8.845 110gi|13182974 NA 7.793 8.895 8.712 8.321 111 NM_198336 INSIG1 8.081 7.3708.062 7.867 112 ENST00000542161 FOLR3 7.767 8.283 8.505 9.059 113NM_001024630 RUNX2 9.359 9.363 8.874 8.973 114 NM_018457 PRR13 8.9199.881 9.380 9.204 115 NM_003546 HIST1H4L 9.807 7.908 9.466 9.602 116NM_002305 LGALS1 10.393 10.059 10.730 10.741 117 NM_001295 CCR1 9.0369.458 10.071 10.000 118 NM_003596 TPST1 8.526 10.334 9.295 9.600 119NM_019111 HLA-DRA 11.467 10.758 10.870 10.441 120 NM_004244 CD163 6.8238.730 7.652 7.643 121 NM_005306 FFAR2 9.559 9.849 10.479 10.309 122NM_001143804 PHOSPH01 11.398 10.826 10.374 9.837 123 NM_005729 PPIF9.131 8.392 8.994 8.669 124 NM_001199760 MTHFS 8.177 9.060 8.644 8.530125 NM_015190 DNAJC9 7.382 5.889 6.594 7.251 126 NM_005564 LCN2 7.7298.168 8.921 10.113 127 ENST00000233057 EIF2AK2 7.237 8.670 8.347 8.159128 NM_006498 LGALS2 6.920 6.085 6.594 6.235 129 NM_001199922 SIAE 6.7216.530 7.174 7.284 130 NM_004644 AP3B2 5.979 6.181 6.485 6.665 131NM_152701 ABCA13 5.814 6.131 6.688 7.350 132 gi|21757933 NA 7.336 7.9637.065 7.340 133 NR_026586 EFCAB2 4.462 4.942 5.648 4.939 134 NM_170745HIST1H2AA 6.310 6.872 6.483 6.652 135 NR_024610 HINT1 7.948 6.705 7.5917.235 136 NM_003535 HIST1H3J 8.323 6.703 7.484 7.314 137 NM_001785 CDA10.407 11.415 11.045 11.032 138 NM_003864 SAP30 9.022 9.883 9.258 9.251139 NM_001040196 AGTRAP 10.055 9.933 10.594 10.356 140 NM_033050 SUCNR13.660 3.734 4.329 4.517 141 NM_002454 MTRR 8.163 7.862 8.523 8.796 142NM_001168357 PLA2G7 6.797 6.685 5.928 5.753 143 NM_016108 AIG1 6.2986.237 6.932 6.735 144 NM_013363 PCOLCE2 5.863 5.909 6.292 6.366 145NM_080491 GAB2 9.471 10.280 9.787 9.810 146 NM_012262 HS2ST1 7.018 6.8747.363 7.378 147 NM_003529 HIST1H3A 7.822 6.477 7.240 7.007 148gi|21757754 C22orf37 8.047 7.525 8.170 7.898 149 ENST00000443117HLA-DPA1 11.917 11.327 11.426 10.930 150 NM_030796 VOPP1 9.302 8.7719.510 9.318 151 NM_001135147 SLC39A8 7.820 7.364 8.061 7.951 152NM_002417 MKI67 5.979 5.822 6.140 6.894 153 NM_000578 SLC11A1 10.81211.719 11.333 11.110 154 NM_001657 AREG 6.075 6.806 6.139 6.126 155NM_005502 ABCA1 7.734 7.909 8.601 8.816 156 NM_001201427 DAAM2 6.5918.217 7.135 7.167 157 NM_002343 LTF 8.098 8.330 8.923 9.937 158NM_178174 TREML1 8.869 9.169 9.868 10.187 159 NM_004832 GSTO1 7.1137.036 7.593 7.598 160 NM_000956 PTGER2 8.918 8.348 9.019 8.983 161NM_001816 CEACAM8 7.336 7.874 8.503 9.775 162 NM_016184 CLEC4A 8.2108.289 9.043 8.708 163 NR_002217 PMS2CL 7.598 6.664 7.289 7.057 164NM_001193374 RETN 7.747 7.886 8.097 8.248 165 NM_000922 PDE3B 8.1428.242 7.627 7.734 166 NM_018837 SULF2 9.831 9.704 9.064 9.248 167NM_001145001 NEK6 9.503 9.287 9.835 9.710 168 NM_022145 CENPK 7.0736.142 6.664 6.040 169 NM_145725 TRAF3 8.046 7.482 8.145 8.152 170NM_003608 GPR65 9.085 9.452 9.834 9.216 171 NR_046000 IRF4 8.200 7.4917.843 8.381 172 gi|42521648 MACF1 6.473 6.545 7.013 7.187 173 NM_001144AMFR 9.420 8.994 9.671 9.823 174 NM_000985 RPL17 6.122 5.182 5.758 5.556175 NM_003749 IRS2 8.965 9.531 8.841 8.998 176 NM_002230 JUP 8.165 7.8037.812 7.804 177 NM_013230 CD24 5.563 5.793 6.173 7.205 178 NM_004481GALNT2 8.541 8.534 8.896 8.980 179 NM_007355 HSP90AB1 9.881 8.635 9.0859.720 180 NM_024656 GLT25D1 9.757 9.336 9.948 9.861 181 NM_001001658OR9A2 4.207 4.662 4.825 4.991 182 NM_001178135 HDHD1A 8.039 7.927 8.2968.300 183 NM_001141945 ACTA2 6.977 6.906 7.430 7.434 184 NM_152282 ACPL26.821 7.762 7.139 7.253 185 NM_001137550 LRRFIP1 6.512 6.814 6.827 7.037186 NM_001161352 KCNMA1 5.664 6.042 6.492 6.317 187 gi|12584148 OCR18.817 9.952 8.387 8.844 188 NM_000885 ITGA4 8.779 7.932 8.394 7.981 189NM_001412 EIF1AX 7.439 6.463 7.251 6.667 190 NM_176818 SFRS9 9.739 9.6669.715 10.226 191 NM_206831 DPH3 6.211 6.602 6.923 6.606 192 NM_001031711ERGIC1 9.539 10.203 9.742 9.565 193 NM_007261 CD300A 9.890 9.479 10.05810.054 194 NM_001085386 NF-E4 7.348 8.202 7.715 8.545 195 NM_004897MINPP1 8.212 7.697 8.228 7.151 196 NM_003141 TRIM21 8.072 8.151 8.8408.371 197 NM_006969 ZNF28 4.936 4.511 5.008 4.554 198 gi|21538810 NPCDR15.404 5.022 4.784 5.166 199 gi|15530286 NA 9.276 9.224 8.804 8.909 200gi|7021995 NA 7.607 8.002 6.917 7.075 201 NM_000201 ICAM1 8.842 8.6259.470 9.156 202 NM_005645 TAF13 5.332 5.173 5.949 5.474 203 NM_000917P4HA1 6.365 6.096 6.773 6.664 204 NM_207445 C15orf54 4.953 4.588 4.3944.290 205 NM_002108 HAL 7.142 6.909 7.654 7.331 206 NM_015998 KLHL59.003 9.971 9.113 9.407 207 NR_002612 DLEU2 6.549 6.894 7.347 6.699 208NM_015199 ANKRD28 7.286 7.393 7.898 7.722 209 ENST00000375864 LY6G5B9.037 8.992 8.780 8.654 210 ENST00000344062 KIAA1257 6.868 7.365 6.8826.874 211 NM_004528 MGST3 9.104 8.519 9.273 9.098 212 NM_015187 KIAA07468.174 7.591 8.170 8.747 213 NM_001540 HSPB1 9.140 8.923 9.664 9.580 214NM_005508 CCR4 7.105 6.356 6.598 6.829 215 NM_001071 TYMS 6.084 5.6956.186 6.854 216 ENST00000536831 RRP12 8.946 8.381 8.821 8.752 217NM_176816 CCDC125 7.600 8.401 7.883 8.048 218 NM_003521 HIST1H2BM 10.1049.242 10.213 10.837 219 NM_002612 PDK4 7.445 8.411 8.080 8.035 220NM_207627 ABCG1 8.318 7.923 7.960 8.214 221 NM_000576 IL1B 9.070 9.17210.021 9.550 222 NM_003246 THBS1 8.599 9.860 8.993 9.423 223 NM_000419ITGA2B 8.899 8.768 9.482 9.747 224 NM_005780 LHFP 6.216 6.391 6.5236.665 225 NM_002287 LAIR1 9.265 9.118 9.815 9.654 226 NM_003537 HIST1H3B8.783 7.684 8.739 9.501 227 gi|29387167 ZRANB1 8.205 9.041 8.641 8.455228 ENST00000525158 TIMM10 7.454 6.704 7.473 7.175 229 NM_207647 FSD1L4.605 4.402 4.801 4.565 230 NM_021066 HIST1H2AJ 5.699 4.152 4.990 3.871231 ENST00000362012 PTGS1 8.883 8.605 9.293 9.429 232 gi|14250459 NA6.389 5.970 6.875 5.976 233 NM_080678 UBE2F 7.698 7.618 8.235 8.225 234NM_001104595 FAM118A 8.366 7.706 7.946 8.222 235 NM_003531 HIST1H3C9.254 7.630 9.025 9.427 236 NM_003965 CCRL2 6.401 6.488 6.982 6.754 237NR_003094 E2F6 4.235 3.673 4.143 3.579 238 NM_198275 MPZL3 10.241 10.75410.209 10.039 239 NM_080725 SRXN1 9.497 9.560 9.732 9.815 240 NM_004357CD151 9.083 8.712 9.309 9.522 241 NM_003536 HIST1H3H 9.933 8.623 9.6889.538 242 NM_031919 FSD1L 2.752 2.614 2.977 2.804 243 NM_001131065 RFESD6.745 6.242 6.502 5.562 244 NM_012112 TPX2 5.722 5.535 5.917 6.431 245NM_006272 S100B 5.289 4.661 4.917 4.533 246 NM_032828 ZNF587 8.514 8.8168.783 8.381 247 NM_152501 PYHIN1 8.390 8.040 7.744 7.890 248 NM_020775KIAA1324 9.383 9.433 8.708 9.010 249 NM_002483 CEACAM6 5.249 5.373 5.9596.957 250 NM_001130415 APOLD1 7.318 6.858 7.205 7.363 251 NM_000134FABP2 4.244 4.518 4.396 4.549 252 NM_001080424 KDM6B 9.854 10.383 10.1479.749 253 ENST00000390265 IGK@ 10.393 9.201 10.307 11.107 254 NM_006097MYL9 9.235 9.445 10.021 9.931 255 NM_021058 HIST1H2BJ 5.949 5.710 6.3306.446 256 NM_138327 TAAR1 5.009 5.081 5.282 5.490 257 NM_001828 CLC10.866 9.692 9.618 9.860 258 NM_001199208 CYP4F3 9.187 10.093 9.4029.895 259 NM_024548 CEP97 6.566 6.717 6.887 7.228 260 NM_138927 SON8.449 7.921 8.330 7.930 261 NM_002198 IRF1 10.490 10.067 10.764 10.511262 NM_182914 SYNE2 7.764 8.208 7.453 7.752 263 NM_000902 MME 9.62510.744 9.401 10.054 264 NM_024552 LASS4 8.222 7.808 7.961 8.050 265NM_001925 DEFA4 7.421 7.383 8.289 9.454 266 NM_024913 C7orf58 7.7276.800 7.706 7.617 267 ENST00000549649 DYNLL1 7.250 7.328 8.200 7.946 268gi|38532374 NA 5.269 5.274 4.828 4.979 269 NM_000250 MPO 7.605 7.5658.053 8.694 270 NM_001874 CPM 5.874 6.569 5.926 5.965 271 NM_173485TSHZ2 7.382 6.972 6.846 7.429 272 NR_038064 PLIN2 8.210 8.534 8.4708.589 273 NM_024556 FAM118B 7.287 7.256 7.846 7.892 274 NM_001199873B4GALT3 9.539 8.790 9.278 9.265 275 NM_006989 RASA4 8.298 8.139 7.7968.031 276 NM_001257971 CTSL1 6.074 5.925 6.577 6.106 277 NM_000270 NP9.487 9.103 9.780 10.001 278 NM_001130059 ATF7 5.212 5.212 5.253 5.605279 NM_003118 SPARC 9.083 9.207 9.737 9.964 280 NM_153021 PLB1 6.8677.077 7.318 7.615 281 NM_001170330 C4orf3 7.478 7.240 7.324 7.402 282NM_002692 POLE2 7.205 6.613 7.184 6.047 283 NM_001192 TNFRSF17 4.4744.005 4.587 5.008 284 NM_145032 FBXL13 6.474 6.987 6.791 7.387 285NM_019091 PLEKHA3 7.058 6.910 7.281 6.689 286 NM_024956 TMEM62 7.5997.189 7.875 7.664 287 NM_052960 RBP7 7.270 7.808 7.218 7.267 288NM_024613 PLEKHF2 7.432 7.660 8.044 7.255 289 NM_002923 RGS2 11.58412.239 11.737 11.649 290 NM_004691 ATP6V0D1 11.562 11.584 11.951 11.656291 NM_144563 RPIA 9.444 9.221 8.913 8.388 292 NM_020397 CAMK1D 9.0019.118 8.603 8.572 293 NM_016232 IL1RL1 5.573 6.273 5.757 6.219 294NM_138460 CMTM5 7.473 7.266 7.853 7.851 295 NM_004847 AIF1 8.167 8.3108.735 8.045 296 NM_001928 CFD 10.389 9.779 9.631 9.496 297 NM_144765MPZL2 7.015 7.320 6.845 6.660 298 gi|27884043 LOC100128751 8.552 8.8778.333 8.552 299 NM_144646 IGJ 8.646 7.900 8.962 10.005 300 NM_139286CDC26 7.968 7.634 8.113 7.886 301 NM_006241 PPP1R2 7.446 7.738 7.5467.286 302 NM_000564 IL5RA 6.871 6.298 6.092 6.455 303 NM_001113738ARL17P1 8.846 8.829 8.802 8.293 304 NR_033759 ATP5L 7.242 7.337 7.3796.824 305 NM_176885 TAS2R31 6.228 5.589 5.847 6.020 306 NM_001024599HIST2H2BF 8.854 9.615 9.386 9.211 307 NM_001743 CALM2 8.475 9.041 9.0038.357 308 NM_019073 SPATA6 6.797 7.301 7.095 6.806 309 ENST00000390285IGLV6-57 5.779 5.379 6.029 6.575 310 NM_020362 C1orf128 9.258 9.0268.605 8.315 311 NM_181623 KRTAP15-1 6.250 6.690 6.548 6.617 312NM_006417 IFI44 6.559 6.924 8.107 6.674 313 NM_001178126 IGL@ 7.0526.606 7.027 7.343 314 gi|21707823 NA 4.903 5.476 4.955 4.639 315NM_003001 SDHC 7.530 6.874 7.879 7.593 316 NM_152995 NFXL1 7.326 6.8767.694 7.644 317 NM_000170 GLDC 5.599 5.395 5.679 5.770 318 NM_001199743DCTN5 8.646 8.336 8.737 8.794 319 NM_014736 KIAA0101 4.443 3.951 4.5374.749 pval pval pval pval SEQ ID Shock pval HC inSIRS vs Severe vs Shockvs Shock vs Number Mean vs Other Sepsis Mild Mild Severe  1 10.3110.000000 0.000225 0.275728 0.000244 0.110996  2 12.044 0.000000 0.0870610.048910 0.027926 0.991139  3 10.876 0.000000 1.000000 0.087388 0.0024020.671136  4 10.335 0.000000 0.000015 0.817659 0.104460 0.049488  5 6.7230.000000 0.000000 0.645365 0.004479 0.136619  6 11.388 0.000000 0.0029790.095954 0.690976 0.351809  7 9.828 0.000000 0.383427 0.945300 0.0340260.035853  8 10.593 0.000000 0.000016 0.650271 0.142678 0.725241  9 7.6110.000000 0.009068 0.118387 0.000147 0.212773  10 8.919 0.000000 1.0000000.628534 0.008931 0.209877  11 10.273 0.000000 0.001046 0.2842010.980298 0.366022  12 7.293 0.000000 0.000000 0.322762 0.032568 0.722429 13 9.813 0.000000 0.000434 0.708180 0.000658 0.034215  14 8.4080.000000 0.020098 0.014566 0.000045 0.511511  15 7.075 0.000000 0.0000000.407446 0.422801 0.051201  16 6.063 0.000000 0.000000 0.486295 0.0014840.125031  17 9.990 0.000000 1.000000 0.200617 0.035680 0.892137  186.839 0.000000 1.000000 0.251812 0.000431 0.163472  19 11.148 0.0000000.000000 0.861884 0.017391 0.144729  20 7.385 0.000000 0.000000 0.2481950.001153 0.259068  21 10.055 0.000000 0.000079 0.962640 0.8372390.731337  22 8.464 0.000000 1.000000 0.732360 0.001453 0.000582  239.945 0.000000 0.000082 0.796454 0.024690 0.225126  24 8.097 0.0000001.000000 0.373616 0.001873 0.205385  25 9.050 0.000000 0.000000 0.3629610.132905 0.008450  26 8.855 0.000000 0.431436 0.880592 0.070482 0.316561 27 10.368 0.000000 0.000000 0.333550 0.115281 0.005971  28 10.7500.000000 0.035769 0.441852 0.011707 0.389198  29 9.583 0.000000 0.0018940.989573 0.184680 0.221197  30 7.532 0.000000 0.000080 0.007994 0.1415340.338948  31 10.019 0.000000 0.000133 0.872838 0.059398 0.037699  328.384 0.000000 0.000062 0.830987 0.170622 0.085371  33 7.577 0.0000000.007043 0.500548 0.040553 0.570947  34 6.470 0.000000 1.000000 0.3059820.011176 0.000339  35 8.878 0.000000 0.000000 0.833718 0.133216 0.512817 36 9.738 0.000000 1.000000 0.712067 0.908467 0.905260  37 7.6490.000000 0.000000 0.417434 0.196375 0.968937  38 11.830 0.0000001.000000 0.196819 0.086637 0.992129  39 9.928 0.000000 0.000039 0.0004320.001207 0.709854  40 4.543 0.000000 0.007278 0.033428 0.021421 0.979558 41 6.450 0.000000 0.652812 0.064249 0.026676 0.999993  42 6.7380.000000 0.000000 0.074815 0.001252 0.606835  43 8.128 0.000000 1.0000000.670176 0.001769 0.073073  44 8.548 0.000000 1.000000 0.438680 0.0131490.000955  45 5.937 0.000000 1.000000 0.493837 0.002433 0.158402  4610.196 0.000000 0.069041 0.999591 0.014557 0.037351  47 10.284 0.0000000.000000 0.500534 0.001958 0.138243  48 8.037 0.000000 0.000000 0.3154450.060594 0.854279  49 10.323 0.000000 0.000004 0.695152 0.0552540.448113  50 9.243 0.000000 0.000039 0.180477 0.821921 0.417016  519.669 0.000000 0.000000 0.999875 0.029147 0.070614  52 8.227 0.0000000.000000 0.350572 0.002949 0.273300  53 7.900 0.000000 0.000000 0.7984080.423055 0.895083  54 9.674 0.000000 0.000001 0.091052 0.855508 0.225820 55 7.237 0.000000 0.000000 0.536662 0.490374 0.996684  56 7.5260.000000 0.002476 0.799183 0.046887 0.320589  57 5.906 0.000000 1.0000000.002710 0.877917 0.009767  58 9.578 0.000000 0.067333 0.026428 0.0000130.253098  59 8.353 0.000000 0.297018 0.000084 0.000000 0.708038  608.227 0.000000 0.000000 0.609142 0.000879 0.060210  61 9.860 0.0000000.020087 0.998663 0.799026 0.869508  62 9.150 0.000000 0.000000 0.0009840.661905 0.010409  63 7.279 0.000000 0.000000 0.986016 0.997776 0.974877 64 7.373 0.000000 1.000000 0.993673 0.000056 0.000352  65 9.3220.000000 0.000068 0.003150 0.006691 0.784808  66 7.047 0.000000 0.0000000.428924 0.022397 0.521806  67 8.420 0.000000 0.000290 0.005182 0.0019320.986095  68 9.657 0.000000 0.000000 0.761189 0.939475 0.910197  699.813 0.000000 0.000063 0.216377 0.819582 0.075588  70 7.416 0.0000000.245652 0.024855 0.001421 0.884318  71 5.712 0.000000 1.000000 0.0000210.000000 0.945115  72 11.772 1.000000 0.000000 0.950268 0.1729350.420732  73 10.159 0.000000 0.000000 0.156468 0.002260 0.485665  748.937 0.000000 0.000000 0.958822 0.401504 0.346256  75 6.845 0.0000000.417891 0.196267 0.015651 0.756446  76 9.015 0.000000 0.000000 0.8685730.011535 0.108528  77 6.815 0.000000 1.000000 0.344640 0.006675 0.394595 78 8.804 0.000000 0.000000 0.777976 0.039725 0.310273  79 7.6350.000000 1.000000 0.276607 0.002832 0.000063  80 6.214 0.000000 0.0000000.002936 0.000187 0.968287  81 7.376 0.000001 0.000004 0.000039 0.0003640.493398  82 6.047 0.000000 0.490985 0.040847 0.969911 0.066504  837.466 0.000000 0.000000 0.970637 0.064634 0.196970  84 7.632 0.0000000.000000 0.387020 0.434149 0.966275  85 9.307 0.000000 1.000000 0.0263250.003375 0.962707  86 7.946 0.000000 0.000024 0.895298 0.035905 0.027021 87 6.520 0.000000 0.976589 0.222869 0.013639 0.687542  88 4.1350.000000 0.000000 0.020091 0.996732 0.024012  89 6.824 0.000000 0.9149210.404820 0.968067 0.529328  90 10.570 0.041844 0.000000 0.5843950.561379 0.994393  91 9.589 0.000000 0.026705 0.001145 0.046592 0.253799 92 8.165 0.000000 0.001975 0.857762 0.030361 0.018763  93 5.7830.000000 1.000000 0.937302 0.322103 0.253166  94 10.249 0.0000001.000000 0.218914 0.132537 0.999989  95 6.492 0.000000 1.000000 0.1256880.003063 0.604976  96 8.952 0.000000 1.000000 0.109345 0.035683 0.988342 97 7.961 0.000000 0.543009 0.580949 0.154696 0.809916  98 9.3010.000000 0.000012 0.105531 0.985106 0.141523  99 9.340 0.000000 0.0000010.961397 0.985058 0.991536 100 8.641 0.000000 0.000000 0.094884 0.0102010.880791 101 6.870 0.000000 1.000000 0.786615 0.000756 0.026622 1028.910 0.000000 1.000000 0.894388 0.745151 0.981575 103 7.123 0.0000000.011838 0.056985 0.008125 0.944270 104 8.726 0.000000 0.000056 0.4855760.954853 0.641602 105 6.703 0.000000 0.000000 0.872556 0.096505 0.389383106 8.428 1.000000 0.000000 0.034062 0.001276 0.808136 107 8.1120.000000 1.000000 0.310769 0.000008 0.014285 108 6.368 0.000000 0.0162480.998808 0.245788 0.323554 109 9.307 0.000000 0.000000 0.796042 0.0027540.059157 110 8.114 0.000000 0.003518 0.039103 0.000094 0.391472 1117.913 0.001237 0.000000 0.123875 0.197540 0.883915 112 9.163 0.0000000.039807 0.074391 0.008772 0.909407 113 8.703 0.000000 0.000000 0.6839140.223731 0.064525 114 9.375 0.000000 0.000085 0.390820 0.998964 0.411767115 9.065 0.000032 0.000000 0.878290 0.231084 0.140998 116 10.9380.246894 0.000000 0.995433 0.137821 0.261564 117 10.253 0.0000000.009681 0.937695 0.574455 0.448234 118 9.760 0.000000 0.055822 0.5189070.136190 0.833502 119 10.188 0.000000 1.000000 0.091925 0.0005750.429491 120 8.413 0.000000 0.092636 0.999632 0.037288 0.077812 12110.573 0.000000 0.002329 0.564897 0.791331 0.256398 122 10.185 0.0000000.048380 0.088690 0.661703 0.354179 123 8.682 0.000000 0.001262 0.0160290.006615 0.993122 124 8.897 0.000000 0.406481 0.678016 0.086391 0.022355125 6.665 0.000000 0.000027 0.007512 0.921502 0.019348 126 10.0920.000000 0.000002 0.028722 0.011016 0.998889 127 8.071 0.000000 0.2635280.709245 0.379350 0.929139 128 6.142 0.000000 0.375827 0.045528 0.0019600.806041 129 7.068 0.000300 0.000000 0.666202 0.603123 0.213378 1307.116 0.000000 0.000003 0.646507 0.001477 0.071475 131 7.182 0.0000000.000000 0.066387 0.130673 0.834277 132 7.275 1.000000 0.000000 0.0343220.071492 0.818353 133 5.021 0.000000 1.000000 0.000756 0.000575 0.898330134 6.820 0.000002 0.020011 0.118625 0.000044 0.124349 135 7.0520.000000 0.041290 0.142709 0.003549 0.590766 136 7.135 0.000000 0.0002740.715862 0.160141 0.691744 137 11.020 0.000000 0.043371 0.9965840.982475 0.996794 138 9.295 0.000000 0.001328 0.998061 0.924872 0.921435139 10.502 0.000787 0.000000 0.083006 0.602023 0.382046 140 4.3340.000000 0.000000 0.487465 0.999396 0.504866 141 8.849 0.122045 0.0000000.216837 0.057755 0.942570 142 5.816 0.000000 0.011194 0.534519 0.7085350.921793 143 7.005 0.000001 0.000000 0.470358 0.871019 0.246707 1446.785 0.000000 0.000000 0.925694 0.013235 0.089534 145 9.689 0.0000000.000000 0.982341 0.635805 0.599323 146 7.465 0.001571 0.000000 0.9909090.556564 0.723490 147 6.871 0.000000 0.000513 0.477800 0.090217 0.776827148 7.869 0.152851 0.000000 0.021435 0.002302 0.954990 149 10.5820.000000 1.000000 0.125876 0.000597 0.352649 150 9.517 1.000000 0.0000000.298375 0.997162 0.269787 151 8.434 1.000000 0.000000 0.788039 0.0312260.013434 152 6.463 0.105108 0.000000 0.000163 0.097385 0.045251 15311.214 0.000000 0.000048 0.338648 0.659426 0.788341 154 6.310 0.0013040.023771 0.980680 0.018616 0.030209 155 8.449 0.000000 0.000000 0.5743590.692624 0.204976 156 7.607 0.000000 0.072069 0.992564 0.124949 0.254325157 9.639 0.000000 0.000019 0.012909 0.052398 0.670808 158 9.8760.000000 0.000638 0.449927 0.999311 0.468015 159 7.768 0.000017 0.0000000.999247 0.345252 0.470840 160 9.315 1.000000 0.000000 0.970366 0.0706540.081972 161 9.287 0.000000 0.001298 0.011921 0.098854 0.501991 1629.051 0.000001 0.000030 0.153416 0.998827 0.141809 163 7.040 0.0000000.000440 0.281314 0.146553 0.993528 164 8.835 0.000000 0.000109 0.7671250.000526 0.022922 165 7.730 0.000586 0.000010 0.567468 0.494918 0.999337166 8.696 0.000000 0.000000 0.721156 0.183689 0.059640 167 9.9430.153083 0.000000 0.541334 0.546065 0.125355 168 6.012 0.000000 1.0000000.003871 0.000401 0.988210 169 7.882 1.000000 0.000003 0.997488 0.0117070.028412 170 9.665 0.000000 1.000000 0.000115 0.360571 0.006354 1717.762 0.000105 0.000070 0.002770 0.825556 0.000501 172 7.377 0.0000090.000481 0.609788 0.061186 0.553735 173 9.635 1.000000 0.000000 0.5438470.954865 0.395265 174 5.513 0.000000 0.000708 0.298432 0.096378 0.944801175 9.024 1.000000 0.000068 0.322209 0.133169 0.969809 176 7.4670.000000 1.000000 0.997835 0.006010 0.023849 177 7.139 0.000000 0.0443390.008714 0.004161 0.979397 178 9.333 0.000001 0.000000 0.887956 0.0175210.130219 179 9.186 0.000000 0.052850 0.015572 0.861439 0.050094 18010.201 1.000000 0.000001 0.826970 0.124352 0.061226 181 5.108 0.0000001.000000 0.671976 0.216358 0.816042 182 8.508 0.156481 0.000026 0.9994410.101404 0.187976 183 7.417 0.000137 0.000000 0.999465 0.993242 0.991119184 7.209 0.000000 0.038264 0.736975 0.857652 0.955277 185 7.2410.000015 1.000000 0.216163 0.000656 0.232230 186 7.025 0.000000 0.0313500.804476 0.075606 0.033982 187 8.270 1.000000 0.000000 0.273663 0.8901990.131897 188 7.906 0.000000 1.000000 0.064191 0.007047 0.909442 1896.907 0.000022 0.033776 0.002586 0.056446 0.338019 190 10.128 1.0000000.000618 0.000018 0.000064 0.627141 191 6.842 0.000000 1.000000 0.0523190.764333 0.189405 192 9.579 0.014810 0.000006 0.364984 0.320097 0.993728193 10.025 1.000000 0.000000 0.999363 0.929522 0.957280 194 7.9340.000000 1.000000 0.002063 0.535657 0.030775 195 7.317 0.006705 1.0000000.000005 0.000008 0.707929 196 8.271 0.002474 0.019577 0.006536 0.0000920.782377 197 4.612 0.021118 0.027741 0.000020 0.000018 0.814925 1984.817 0.000001 1.000000 0.000589 0.919120 0.001847 199 8.390 0.0013920.000629 0.821276 0.021614 0.011283 200 6.999 0.480277 0.000000 0.7216940.889525 0.926763 201 9.088 0.069361 0.000002 0.113961 0.015602 0.900519202 5.798 0.054434 0.000001 0.004467 0.457202 0.072895 203 6.8731.000000 0.000000 0.752172 0.731181 0.358637 204 4.370 0.000000 0.8914180.669439 0.972296 0.787051 205 7.571 0.894890 0.000000 0.049449 0.7585720.184215 206 9.083 0.009758 0.000000 0.315312 0.984513 0.248776 2076.850 0.000171 1.000000 0.000019 0.000152 0.504529 208 7.941 0.0000010.046283 0.544926 0.951892 0.391621 209 8.383 0.014059 0.001656 0.6423770.004351 0.135693 210 6.689 1.000000 0.000000 0.997049 0.129430 0.242441211 9.003 1.000000 0.000001 0.377410 0.050613 0.751799 212 8.5021.000000 0.000002 0.015387 0.148674 0.453449 213 9.371 0.267148 0.0000000.845749 0.072845 0.359260 214 6.321 0.000000 1.000000 0.321399 0.1202020.005850 215 6.291 1.000000 0.000000 0.003309 0.816061 0.015734 2168.721 0.000127 0.000063 0.794423 0.522969 0.952728 217 7.988 0.0000290.000070 0.517640 0.700053 0.915690 218 10.261 1.000000 0.0000000.055178 0.976895 0.083087 219 8.318 0.000000 1.000000 0.982556 0.5217420.501879 220 7.791 0.000000 1.000000 0.112606 0.270961 0.003184 2219.930 0.000002 0.124840 0.077098 0.874756 0.185371 222 9.387 0.0000001.000000 0.179953 0.147689 0.987631 223 9.378 0.011285 0.000003 0.4033900.825144 0.174155 224 6.778 0.000000 0.003992 0.475817 0.046096 0.625105225 10.179 0.001517 0.000113 0.773401 0.179212 0.071167 226 8.8521.000000 0.000000 0.042709 0.908040 0.098544 227 8.619 0.000016 0.0017230.476723 0.985358 0.563742 228 6.921 0.000333 0.000431 0.308861 0.0063450.426642 229 5.099 1.000000 0.000000 0.235142 0.050177 0.001077 2304.070 0.000128 1.000000 0.008735 0.013543 0.852098 231 9.247 1.0000000.000001 0.723148 0.952752 0.562936 232 6.059 1.000000 0.638753 0.0000010.000000 0.869569 233 8.425 0.009739 0.003879 0.998629 0.495878 0.558822234 7.802 0.000086 0.879292 0.116376 0.451548 0.008324 235 8.7490.908521 0.000000 0.439119 0.594489 0.101186 236 7.040 0.000011 0.0010070.386796 0.919567 0.226387 237 3.647 0.002951 1.000000 0.000262 0.0002080.874090 238 10.276 1.000000 0.000000 0.418422 0.832036 0.186944 23910.069 0.084230 0.126068 0.777086 0.005812 0.101705 240 9.473 1.0000000.000001 0.408420 0.488688 0.954477 241 9.310 0.018867 0.000017 0.8118220.177187 0.617435 242 3.257 0.713444 0.000000 0.455516 0.068219 0.006227243 5.837 0.011176 1.000000 0.000486 0.005152 0.486482 244 6.0631.000000 0.000000 0.009609 0.587740 0.085624 245 4.692 0.000031 1.0000000.034824 0.206071 0.549376 246 8.101 1.000000 0.003242 0.018153 0.0000010.136854 247 7.473 0.000001 1.000000 0.732308 0.247163 0.086327 2488.061 0.053345 0.004214 0.630998 0.065841 0.013683 249 6.219 0.0000140.000574 0.023214 0.699643 0.121152 250 7.253 0.906687 0.000002 0.2636620.846643 0.520142 251 4.826 0.000004 1.000000 0.578201 0.004767 0.174039252 9.701 1.000000 0.000332 0.072707 0.013999 0.962410 253 10.2281.000000 0.000007 0.087003 0.967489 0.053514 254 9.763 0.000001 0.0174140.905037 0.337602 0.704183 255 6.237 1.000000 0.000001 0.744162 0.7799760.389990 256 5.498 0.000172 0.002470 0.224230 0.119996 0.998003 2579.296 0.000000 1.000000 0.790640 0.575586 0.284504 258 9.361 0.0019980.007361 0.085034 0.976056 0.056129 259 6.933 0.000121 1.000000 0.0353140.917808 0.079681 260 8.146 0.000817 1.000000 0.003077 0.175728 0.168438261 10.387 1.000000 0.000000 0.154204 0.005202 0.633454 262 7.3221.000000 0.000000 0.184100 0.642671 0.032921 263 8.937 1.000000 0.0000000.270540 0.411394 0.024808 264 8.099 0.000006 0.005941 0.629532 0.2321350.868727 265 8.658 0.001307 0.000125 0.042189 0.642870 0.220174 2667.307 0.136406 0.003979 0.899378 0.062794 0.276354 267 7.948 0.0894090.021345 0.534724 0.441022 0.999958 268 4.814 0.188367 0.002418 0.3077600.984538 0.242328 269 8.262 0.003309 0.000013 0.050366 0.644219 0.248933270 6.153 0.263893 0.011698 0.965100 0.195596 0.424691 271 6.9290.004682 1.000000 0.000051 0.739871 0.000534 272 8.747 0.000009 1.0000000.661459 0.056585 0.485502 273 7.670 0.002863 0.002878 0.963051 0.4894330.425727 274 9.060 0.000004 0.006105 0.996920 0.309691 0.456795 2757.741 0.000237 0.794307 0.149393 0.868630 0.057952 276 6.472 0.5561740.000261 0.038526 0.798163 0.134791 277 9.866 1.000000 0.000006 0.5570460.887942 0.802758 278 5.372 1.000000 0.438013 0.000033 0.171523 0.007397279 9.620 0.000424 0.113905 0.627252 0.847118 0.345974 280 7.5390.001516 1.000000 0.250568 0.355397 0.913465 281 7.760 1.000000 0.2454600.750146 0.000037 0.004098 282 6.359 0.156285 1.000000 0.000226 0.0022510.494438 283 4.554 1.000000 0.000000 0.122703 0.982429 0.088234 2846.914 0.000011 1.000000 0.027018 0.806772 0.098170 285 6.825 1.0000001.000000 0.000001 0.000011 0.409707 286 7.625 1.000000 0.002245 0.2541010.080350 0.953292 287 7.088 1.000000 0.000005 0.953122 0.639431 0.530295288 7.671 0.789194 1.000000 0.000012 0.023331 0.029137 289 11.7030.162049 0.000001 0.862749 0.969968 0.947024 290 11.692 0.0640671.000000 0.011327 0.010003 0.932084 291 8.784 0.001733 0.264252 0.0979180.824211 0.261542 292 8.527 0.163494 0.002054 0.980872 0.858346 0.960862293 6.253 0.000028 1.000000 0.065034 0.016357 0.984841 294 7.7161.000000 0.000000 0.999922 0.538365 0.635314 295 8.239 1.000000 1.0000000.000120 0.001571 0.448676 296 9.702 0.000005 1.000000 0.830277 0.9342250.650508 297 6.644 1.000000 0.000023 0.471520 0.309485 0.994504 2988.283 1.000000 0.001039 0.155603 0.877005 0.063053 299 9.230 1.0000000.000100 0.078833 0.793766 0.239952 300 8.065 1.000000 0.000005 0.0959360.867921 0.226701 301 7.113 1.000000 0.001735 0.115620 0.000618 0.379268302 5.954 0.000000 1.000000 0.186587 0.721925 0.044203 303 7.8101.000000 1.000000 0.160174 0.000219 0.191966 304 6.772 1.000000 0.7485160.000612 0.000014 0.929559 305 6.056 0.010741 0.032666 0.488873 0.2534340.969967 306 8.840 0.014281 0.064555 0.679691 0.009289 0.184703 3078.540 1.000000 1.000000 0.000906 0.007024 0.542344 308 7.221 0.0005721.000000 0.238268 0.690136 0.055163 309 6.026 1.000000 0.000002 0.1380730.999915 0.135136 310 8.523 0.000049 0.377958 0.469588 0.921733 0.676477311 6.488 0.001154 1.000000 0.779960 0.773039 0.418346 312 6.4081.000000 1.000000 0.007884 0.000183 0.836520 313 7.023 1.000000 0.0000000.162844 0.999656 0.156176 314 4.576 1.000000 0.188292 0.144998 0.0264710.921677 315 7.764 1.000000 0.000026 0.453417 0.841420 0.752519 3167.620 1.000000 0.000002 0.977232 0.934372 0.994545 317 5.666 1.0000000.000010 0.537664 0.982363 0.443725 318 8.672 1.000000 0.000532 0.8659680.779044 0.519467 319 4.419 1.000000 0.000000 0.416052 0.694594 0.124264

Example 6 Ratios of IRS Biomarkers Markers Between Healthy, inSIRS, MildSepsis, Severe Sepsis and Septic Shock

Examples of the use of 2-gene ratios as a more informative predictor ofclinical condition than either of the two component genes are presentedin Tables 16, 17, 18, 19, 20 and 21. These tables show instances of theprediction of Healthy and inSIRS (Table 16), Healthy vs. ipSIRS (Table17), inSIRS and ipSIRS (Table 18), Mild Sepsis vs.Vs Severe Sepsis(Table 19), Mild Sepsis Vs Septic Shock (Table 20), and Severe Sepsisvs.Vs Septic Shock (Table 21) using 2 genes and their ratios. Columnsfrom left to right are: name of the first component gene (Gene 1 Name),the corresponding Area Under Curve for this gene (Gene 1 AUC), thesecond component gene (Gene 2 Name), the corresponding AUC for this gene(Gene 2 AUC), the AUC for this ratio (Ratio AUC), the statisticalsignificance using Delong's method (DeLong E R, DeLong D M,Clarke-Pearson D L: Comparing the Areas under Two or More CorrelatedReceiver Operating Characteristic Curves: A Non parametric Approach.Biometrics 1988, 44:837-845) that the ratio is a better predictor thanGene 1 (Ratio Signif to Gene 1), the statistical significance usingDelong's method that the ratio is a better predictor than Genet (RatioSignif to Gene 2). These tables show results for which the ratio AUC isshown to be superior to both of the component genes, and the improvementstatistically significant over both genes. Examples of less significantratios, or cases where the ratio is statistically superior to only oneof the component genes are not listed in these tables. Such ratios canalso be used in clinical trials in a similar fashion to that describedin Example 5.

Lengthy table referenced here US20160055295A1-20160225-T00001 Pleaserefer to the end of the specification for access instructions.

Lengthy table referenced here US20160055295A1-20160225-T00002 Pleaserefer to the end of the specification for access instructions.

Lengthy table referenced here US20160055295A1-20160225-T00003 Pleaserefer to the end of the specification for access instructions.

Lengthy table referenced here US20160055295A1-20160225-T00004 Pleaserefer to the end of the specification for access instructions.

Lengthy table referenced here US20160055295A1-20160225-T00005 Pleaserefer to the end of the specification for access instructions.

Lengthy table referenced here US20160055295A1-20160225-T00006 Pleaserefer to the end of the specification for access instructions.

Throughout this specification and claims which follow, unless thecontext requires otherwise, the word “comprise”, and variations such as“comprises” or “comprising”, will be understood to imply the inclusionof a stated integer or group of integers or steps but not the exclusionof any other integer or group of integers.

Persons skilled in the art will appreciate that numerous variations andmodifications will become apparent. All such variations andmodifications, which become apparent to persons skilled in the art,should be considered to fall within the spirit and scope that theinvention broadly appearing before described.

LENGTHY TABLES The patent application contains a lengthy table section.A copy of the table is available in electronic form from the USPTO website(http://seqdata.uspto.gov/?pageRequest=docDetail&DocID=US20160055295A1).An electronic copy of the table will also be available from the USPTOupon request and payment of the fee set forth in 37 CFR 1.19(b)(3).

1. A composition comprising at least one reverse transcribed mRNAselected from a C3AR1 reverse transcribed mRNA and a HLA-DBP1 reversetranscribed mRNA, and at least one oligonucleotide primer or probe thathybridizes to the at least one reverse transcribed mRNA, wherein the atleast one reverse transcribed mRNA is from a subject with a clinicalsign of SIRS.
 2. The composition according to claim 1, wherein the atleast one oligonucleotide primer or probe is hybridized to the at leastone reverse transcribed mRNA.
 3. The composition according to claim 1,wherein the at least one reverse transcribed mRNA is derived from immunecells.
 4. The composition according to claim 1, wherein the at least onereverse transcribed mRNA is derived from leukocytes.
 5. The compositionaccording to claim 1, wherein the at least one reverse transcribed mRNAis derived from blood cells.
 6. The composition according to claim 1,wherein the at least one reverse transcribed mRNA is derived fromperipheral blood cells.
 7. The composition according to claim 1, furthercomprising a labeled reagent for detecting the at least one reversetranscribed mRNA.
 8. The composition according to claim 7, wherein thelabeled reagent is a labeled said at least one oligonucleotide primer orprobe.
 9. The composition according to claim 7, wherein the labeledreagent is a labeled said at least one reverse transcribed mRNA.
 10. Akit for determining the presence or absence of at least one conditionselected from the group consisting of inSIRS and ipSIRS, the kitcomprising (i) a reagent that allows quantification of a polynucleotideexpression product of the C3AR1 gene; and (ii) a reagent that allowsquantification of a polynucleotide expression product of the HLA-DBP1gene.
 11. A method for treating, preventing or inhibiting thedevelopment of inSIRS or ipSIRS in a subject, the method comprising:administering to the subject an effective amount of an agent that treatsor ameliorates the symptoms or reverses or inhibits the development ofinSIRS or ipSIRS on the basis that the subject has an increasedlikelihood of having inSIRS or ipSIRS, as determined by acondition-determining method that comprises: (1) providing a correlationof a reference IRS biomarker profile with the presence or absence, ordegree of a condition selected from a healthy condition, SIRS, inSIRS oripSIRS, wherein the reference IRS biomarker profile evaluates at leastone IRS biomarker; (2) obtaining an IRS biomarker profile of a samplefrom the subject, wherein the sample IRS biomarker profile evaluates foran individual IRS biomarker in the reference IRS biomarker profile acorresponding IRS biomarker; and (3) determining a likelihood of thesubject having or not having the condition based on the sample IRSbiomarker profile and the reference IRS biomarker profile, wherein anindividual IRS biomarker is an expression product of an IRS biomarkergene selected from the group consisting of C3AR1 and HLA-DPB1.
 12. Themethod according to claim 11, wherein the condition-determining methoddetermines the likelihood that SIRS or a healthy condition is present orabsent in the subject, and wherein the condition-determining methodcomprises: 1) providing a correlation of a reference IRS biomarkerprofile with the presence or absence of SIRS or the healthy condition,wherein the reference biomarker profile evaluates at least one IRSbiomarker selected from C3AR1 and HLA-DPB1; (2) obtaining a sample IRSbiomarker profile from the subject, which evaluates for an individualIRS biomarker in the reference IRS biomarker profile a corresponding IRSbiomarker, and (3) determining a likelihood of the subject having or nothaving the healthy condition or SIRS based on the sample IRS biomarkerprofile and the reference IRS biomarker profile.
 13. The methodaccording to claim 11, wherein the condition-determining methoddetermines the likelihood that inSIRS, ipSIRS or a healthy condition ispresent or absent in the subject, and wherein the condition-determiningmethod comprises: 1) providing a correlation of a reference IRSbiomarker profile with the likelihood of having or not having inSIRS,ipSIRS or the healthy condition, wherein the reference biomarker profileevaluates C3AR1; (2) obtaining a sample IRS biomarker profile from thesubject, which evaluates for an individual IRS biomarker in thereference IRS biomarker profile a corresponding IRS biomarker; and (3)determining a likelihood of the subject having or not having inSIRS,ipSIRS or a healthy condition the condition based on the sample IRSbiomarker profile and the reference IRS biomarker profile.
 14. Themethod according to claim 11, wherein the condition-determining methoddetermines the likelihood that inSIRS or ipSIRS is present or absent inthe subject, and wherein the condition-determining method comprises: 1)providing a correlation of a reference IRS biomarker profile with thelikelihood of having or not having inSIRS or ipSIRS, wherein thereference biomarker profile evaluates C3AR1; (2) obtaining a sample IRSbiomarker profile from the subject, which evaluates for an individualIRS biomarker in the reference IRS biomarker profile a corresponding IRSbiomarker; and (3) determining a likelihood of the subject having or nothaving inSIRS or ipSIRS based on the sample IRS biomarker profile andthe reference IRS biomarker profile.
 15. The method according to claim11, wherein the condition-determining method determines the likelihoodthat a stage of ipSIRS selected from mild sepsis, severe sepsis andseptic shock is present or absent the subject, and wherein thecondition-determining method comprises: 1) providing a correlation of areference IRS biomarker profile with the likelihood of having or nothaving the stage of ipSIRS, wherein the reference biomarker IRSbiomarker profile evaluates HLA-DPB1; (2) obtaining a sample IRSbiomarker profile from the subject, which evaluates for an individualIRS biomarker in the reference IRS biomarker profile a corresponding IRSbiomarker; and (3) determining a likelihood of the subject having or nothaving the stage of ipSIRS based on the sample IRS biomarker profile andthe reference IRS biomarker profile.
 16. The method according to claim11, wherein an individual IRS biomarker is selected from the groupconsisting of: (a) a polynucleotide expression product comprising anucleotide sequence that shares at least 90% (or at least 91% to atleast 99% and all integer percentages in between) sequence identity withthe sequence set forth in any one of SEQ ID NO: 8 and 58, or acomplement thereof; (b) a polynucleotide expression product comprising anucleotide sequence that encodes a polypeptide comprising the amino acidsequence set forth in any one of SEQ ID NO:327 and 375; (c) apolynucleotide expression product comprising a nucleotide sequence thatencodes a polypeptide that shares at least 90% (or at least 91% to atleast 99% and all integer percentages in between) sequence identity withat least a portion of the sequence set forth in SEQ ID NO:327 and 375;and (d) a polynucleotide expression product comprising a nucleotidesequence that hybridizes to the sequence of (a), (b), (c) or acomplement thereof, under high stringency conditions.
 17. The methodaccording to claim 12, wherein an individual IRS biomarker is selectedfrom the group consisting of: (a) a polynucleotide expression productcomprising a nucleotide sequence that shares at least 90% (or at least91% to at least 99% and all integer percentages in between) sequenceidentity with the sequence set forth in any one of SEQ ID NO: 8 and 58,or a complement thereof; (b) a polynucleotide expression productcomprising a nucleotide sequence that encodes a polypeptide comprisingthe amino acid sequence set forth in any one of SEQ ID NO:327 and 375;(c) a polynucleotide expression product comprising a nucleotide sequencethat encodes a polypeptide that shares at least 90% (or at least 91% toat least 99% and all integer percentages in between) sequence identitywith at least a portion of the sequence set forth in SEQ ID NO:327 and375; and (d) a polynucleotide expression product comprising a nucleotidesequence that hybridizes to the sequence of (a), (b), (c) or acomplement thereof, under high stringency conditions.
 18. The methodaccording to claim 13, wherein an individual IRS biomarker is selectedfrom the group consisting of: (a) a polynucleotide expression productcomprising a nucleotide sequence that shares at least 90% (or at least91% to at least 99% and all integer percentages in between) sequenceidentity with the sequence set forth in any one of SEQ ID NO: 8 and 58,or a complement thereof; (b) a polynucleotide expression productcomprising a nucleotide sequence that encodes a polypeptide comprisingthe amino acid sequence set forth in any one of SEQ ID NO:327 and 375;(c) a polynucleotide expression product comprising a nucleotide sequencethat encodes a polypeptide that shares at least 90% (or at least 91% toat least 99% and all integer percentages in between) sequence identitywith at least a portion of the sequence set forth in SEQ ID NO:327 and375; and (d) a polynucleotide expression product comprising a nucleotidesequence that hybridizes to the sequence of (a), (b), (c) or acomplement thereof, under high stringency conditions.
 19. The methodaccording to claim 14, wherein an individual IRS biomarker is selectedfrom the group consisting of: (a) a polynucleotide expression productcomprising a nucleotide sequence that shares at least 90% (or at least91% to at least 99% and all integer percentages in between) sequenceidentity with the sequence set forth in any one of SEQ ID NO: 8 and 58,or a complement thereof; (b) a polynucleotide expression productcomprising a nucleotide sequence that encodes a polypeptide comprisingthe amino acid sequence set forth in any one of SEQ ID NO:327 and 375;(c) a polynucleotide expression product comprising a nucleotide sequencethat encodes a polypeptide that shares at least 90% (or at least 91% toat least 99% and all integer percentages in between) sequence identitywith at least a portion of the sequence set forth in SEQ ID NO:327 and375; and (d) a polynucleotide expression product comprising a nucleotidesequence that hybridizes to the sequence of (a), (b), (c) or acomplement thereof, under high stringency conditions.
 20. The methodaccording to claim 15, wherein an individual IRS biomarker is selectedfrom the group consisting of: (a) a polynucleotide expression productcomprising a nucleotide sequence that shares at least 90% (or at least91% to at least 99% and all integer percentages in between) sequenceidentity with the sequence set forth in any one of SEQ ID NO: 8 and 58,or a complement thereof; (b) a polynucleotide expression productcomprising a nucleotide sequence that encodes a polypeptide comprisingthe amino acid sequence set forth in any one of SEQ ID NO:327 and 375;(c) a polynucleotide expression product comprising a nucleotide sequencethat encodes a polypeptide that shares at least 90% (or at least 91% toat least 99% and all integer percentages in between) sequence identitywith at least a portion of the sequence set forth in SEQ ID NO:327 and375; and (d) a polynucleotide expression product comprising a nucleotidesequence that hybridizes to the sequence of (a), (b), (c) or acomplement thereof, under high stringency conditions.